Compressed acquisition of microscopic images

Abstract

To provide compressed acquisition of suitable microscopic images.SOLUTION: A microscope for computational imaging may include an illumination source configured to illuminate a sample with a plurality of wavelengths, an image sensor, an objective lens to image the sample onto the image sensor, and a processor operatively coupled to the illumination assembly and the image sensor. The processor may be configured to acquire a first image dataset from the sample illuminated using a first set of illumination conditions at a first wavelength. The processor may also be configured to acquire a second image dataset from the sample illuminated using a second set of illumination conditions having a second number of illumination conditions at a second wavelength. The second set of illumination conditions comprises fewer illumination conditions than the first set in order to decrease acquisition time. The processor may be configured to combine the first and second image datasets into a computationally reconstructed image of the sample.SELECTED DRAWING: Figure 3

Description

[Background technology]

[0001] (CROSS-REFERENCE TO RELATED APPLICATIONS) This application claims the benefit under 35 U.S.C. § 119(e) of U.S. Provisional Patent Application No. 62 / 783,527, filed December 21, 2018, the disclosure of which is incorporated herein by reference in its entirety.

[0002] (background) In computational imaging, a high-resolution computationally reconstructed image of an object can be generated from a series of low-resolution images taken with various illuminations. This approach has the benefit of providing a high-resolution computationally reconstructed image of a sample from an imaging system with lower resolution. Computational imaging can be used to generate a high-resolution computationally reconstructed color image from the low-resolution images. However, the overhead associated with computational imaging is not ideal due to the time required to collect multiple low-resolution images and computationally generate a high-resolution image from the collected information. Because the number of low-resolution images can typically determine the quality and resolution of the output high-resolution image, it can be difficult, at least in some cases, to reduce the overhead without substantially degrading the output image. For applications requiring multiple fields of view, the overhead can be particularly significant.

[0003] Because computational imaging algorithms often rely on relatively good models of physical systems (which may be known, learned, or implied), these algorithms may be wavelength dependent. This wavelength dependency may result in additional overhead in some cases, for example, when each wavelength is processed separately. Reconstruction time may be duplicated for each wavelength, and in some cases, acquisition time may also be duplicated. For example, a color imaging process may operate using three color channels (e.g., red, green, and blue, or "RGB"), which may result in a three-fold increase in acquisition time and a three-fold increase in computational reconstruction time. These channels may also be used in another color space, such as Lab (e.g., CIELab), YCbCr, YUV, or equivalent, which may further add computational reconstruction and / or acquisition time.

[0004] In light of the above, it would be desirable to reduce acquisition and / or reconstruction times without significantly degrading the quality and usefulness of the output computationally reconstructed image. Summary of the Invention [Means for solving the problem]

[0005] (summary) As will be described in more detail below, the present disclosure describes various systems and methods for compressed acquisition of microscopy images by acquiring a first image dataset of a sample using a first set of illumination conditions for a first wavelength and acquiring a second image dataset of the sample using a second set of illumination conditions for a second wavelength. The first set of illumination conditions may include a greater number of illumination conditions than the second set of illumination conditions. The first and second image datasets may be combined into a computationally reconstructed image of the sample. Because the second set of illumination conditions is fewer than the first set of illumination conditions, this approach may reduce acquisition time, reconstruction time, storage requirements, and other costs compared to conventional approaches. Additionally, because the first wavelength may be selected to produce an image containing more information than an image at the second wavelength, the resulting reconstructed image may not contain significantly less information than an image reconstructed from conventional approaches.

[0006] Additionally, the systems and methods described herein may improve the functionality of computing devices (e.g., connected to or integrated with a microscope) by reducing dataset sizes and more efficiently computing reconstructed images. These systems and methods may also improve the field of microscopy imaging by improving acquisition times.

[0007] (Incorporated by reference) All patents, applications, and publications referenced and identified herein are incorporated herein by reference in their entirety and shall be considered to be incorporated by reference in their entirety even if referenced elsewhere in this application.

[0008] A better understanding of the features, advantages, and principles of the present disclosure will be obtained by reference to the following detailed description that sets forth illustrative embodiments and the accompanying drawings. The present invention provides, for example, the following. (Item 1) 1. A method for generating a computationally reconstructed image, comprising: obtaining, with an image sensor, a first image data set from a sample illuminated using a first set of illumination conditions comprising a first number of illumination conditions, each comprising a first wavelength; obtaining, with the image sensor, a second image data set from the sample illuminated using a second set of illumination conditions comprising a second number of illumination conditions, each comprising a second wavelength, wherein the first number is greater than the second number; combining the first image data set and the second image data set into a computationally reconstructed image of the sample; A method comprising: (Item 2) 2. The method of claim 1, wherein the computationally reconstructed image comprises a first spatial frequency bandwidth corresponding to the first wavelength and a second spatial frequency bandwidth corresponding to the second wavelength, the first spatial frequency bandwidth being greater than the second spatial frequency bandwidth. (Item 3) 3. The method of claim 2, wherein the first spatial frequency bandwidth exceeds the spatial frequency bandwidth of one or more images acquired by the image sensor using the first set of illumination conditions at the first wavelength. (Item 4) Item 3. The method of item 2, wherein the second spatial frequency bandwidth exceeds the spatial frequency bandwidth of one or more images acquired by the image sensor using the second set of illumination conditions at the second wavelength. (Item 5) Item 3. The method of item 2, wherein the computationally reconstructed image comprises one or more of increased contrast or aberration correction for the second wavelength based at least in part on the first image data set from the first wavelength. (Item 6) 2. The method of claim 1, further comprising providing the reconstructed image on a display using a first spatial frequency bandwidth and a first user-perceivable color corresponding to the first wavelength and a second spatial frequency bandwidth and a second user-perceivable color corresponding to the second wavelength, wherein the first spatial frequency bandwidth exceeds the second spatial frequency bandwidth and a spatial frequency bandwidth of one or more images obtained by the image sensor using the first set of lighting conditions at the first wavelength. (Item 7) 2. The method of claim 1, wherein the first number is at least two times greater than the second number, a second acquisition time associated with the second image data set is less than or equal to half of a first acquisition time associated with the first image data set, and a spatial frequency bandwidth of the computationally reconstructed image exceeds a spatial frequency bandwidth of each of a plurality of images acquired using the first set of illumination conditions by at least 1.5 times. (Item 8) Item 10. The method of item 1, wherein the first image dataset comprises a plurality of images of the sample, and the second image dataset comprises one or more images of the sample, the second image dataset comprising fewer images of the sample than the first image dataset. (Item 9) Item 10. The method of item 1, wherein the computationally reconstructed image comprises one or more of increased spatial frequency bandwidth, correction for optical aberrations, or increased image contrast. (Item 10) 10. The method of claim 9, wherein the first image data set is transformed into a spatial frequency space and mapped to spatial frequencies within the spatial frequency space based on the first set of illumination conditions to provide the increased spatial frequency bandwidth of the computationally reconstructed image compared to a first spatial frequency bandwidth of each of a plurality of images obtained using the first set of illumination conditions. (Item 11) 10. The method of claim 9, wherein the image sensor has a spatial frequency bandwidth, and the increased spatial frequency bandwidth of the computationally reconstructed image exceeds the spatial frequency bandwidth of the image sensor divided by a magnification of the image of the sample onto the image sensor. (Item 12) 10. The method of claim 9, wherein the first image data set comprises a first plurality of images, each comprising a first spatial frequency bandwidth, and the increased spatial frequency bandwidth of the computationally reconstructed images exceeds the first spatial frequency bandwidth of each of the first plurality of images. (Item 13) Item 13. The method of item 12, wherein the first plurality of images comprise features of the sample that are not resolved using the first spatial frequency bandwidth of each of the first plurality of images, and the computationally reconstructed image comprises features of the sample that are resolved using the increased spatial frequency bandwidth of the computationally reconstructed image. (Item 14) 10. The method of claim 9, wherein the correction of optical aberrations is provided by separating aberration information from sample information so as to reduce the effect of optical aberrations on the computationally reconstructed image, optionally the aberration information comprising aberration spatial frequencies and phases associated with an optical system used to image the sample onto the image sensor, and optionally the sample information comprising sample spatial frequencies and phases associated with a structure of the sample. (Item 15) 10. The method of claim 9, wherein the increased image contrast of the computationally reconstructed image is provided by computationally amplifying high spatial frequencies of the reconstructed image to better represent the sample. (Item 16) 10. The method of claim 9, wherein the computationally reconstructed image comprises the increased contrast, the increased contrast comprising an increased ratio of high to low spatial frequencies in the computationally reconstructed image compared to a ratio of high to low spatial frequencies in each of a plurality of images of the first image dataset. (Item 17) 2. The method of claim 1, wherein the first image dataset and the second image dataset are processed separately to generate a first computationally reconstructed image from the first image dataset and a second computationally reconstructed image from the second dataset, and the first computationally reconstructed image is combined with the second computationally reconstructed image to generate the computationally reconstructed image. (Item 18) 2. The method of claim 1, wherein the first image dataset comprises a first plurality of images and the second image dataset comprises one or more images, and the first plurality of images and the one or more images are processed together to generate the computationally reconstructed image. (Item 19) Item 19. The method of item 18, wherein the one or more images comprise a single image obtained using the second set of lighting conditions. (Item 20) Item 19. The method of item 18, wherein the one or more images comprise a second plurality of images. (Item 21) Item 10. The method of item 1, wherein the computationally reconstructed image comprises a color image, the color image comprising two or more of a red channel, a green channel, or a blue channel. (Item 22) 22. The method of claim 21, wherein the first wavelength corresponds to one of the red channel, the green channel, or the blue channel, and the second wavelength corresponds to another of the red channel, the blue channel, or the green channel. (Item 23) 23. The method of claim 22, wherein the reconstructed image is shown on the display using a first spatial frequency bandwidth for a first color channel corresponding to the first wavelength and a second spatial frequency bandwidth for a second color channel corresponding to the second wavelength, the first spatial frequency bandwidth being greater than the second spatial frequency bandwidth. (Item 24) Item 24. The method of item 23, wherein the first wavelength comprises green light, the first channel comprises the green channel, the second wavelength comprises red light or blue light, the second color channel comprises the red channel or the blue channel, and the green channel is presented on the display using the first spatial frequency bandwidth and the red channel or the blue channel is presented on the display using the second spatial frequency bandwidth. (Item 25) Item 25. The method of item 24, wherein the computationally reconstructed image comprises the red channel, the green channel, and the blue channel, the second wavelength comprises red light corresponding to the red channel, and a third wavelength comprises blue light corresponding to a third channel, and the third channel is shown on the display using a third spatial frequency bandwidth that is less than the first spatial frequency bandwidth. (Item 26) 22. The method of claim 21, wherein the image sensor comprises a sensor with a two-dimensional array of pixels, a first color of the first image data set corresponds to the first wavelength, a second color of the second image data set corresponds to the second wavelength, and the computationally reconstructed image is mapped to the red channel, the green channel, and the blue channel based on the first wavelength and the second wavelength. (Item 27) Item 27. The method of item 26, wherein the image sensor comprises a grayscale image sensor comprising a two-dimensional array of the pixels. (Item 28) Item 27. The method of item 26, wherein the image sensor comprises a color image sensor comprising a two-dimensional array of pixels and a color filter array comprising a plurality of color filters arranged across the two-dimensional array. (Item 29) 29. The method of claim 28, wherein the first image data set is determined based on the first wavelength and a first absorption characteristic of the color filter at the first wavelength, and the second image data set is determined based on the second wavelength and a second absorption characteristic at the second wavelength. (Item 30) 29. The method of claim 28, wherein the first image data set and the second image data set are combined according to a first absorption characteristic of the color filter at the first wavelength and a second absorption characteristic of the color filter at the second wavelength to generate the computationally reconstructed image. (Item 31) 29. The method of claim 28, wherein the first wavelength is different from the second wavelength, and wherein a portion of the first image data set and a portion of the second image data set are obtained substantially simultaneously using one or more of the set of illumination conditions illuminating the sample when the sample is illuminated using one or more of the second set of illumination conditions. (Item 32) Item 10. The method of item 1, wherein the first wavelength is different from the second wavelength. (Item 33) Item 33. The method of item 32, wherein the first wavelength comprises a first color and the second wavelength comprises a second color different from the first color. (Item 34) Item 33. The method of item 32, wherein the first wavelength comprises a first peak of a first illumination source emitting a first distribution of wavelengths, the first distribution of wavelengths comprising a first full width half maximum, and the second wavelength comprises a second peak of a second distribution of wavelengths, the second distribution of wavelengths comprising a second full width half maximum, and the first full width half maximum does not overlap with the second full width half maximum. (Item 35) Item 33. The method according to Item 32, wherein the first wavelength is within one of the following ranges and the second wavelength is within a different one of the following ranges: an ultraviolet range of about 200 to about 380 nanometers (nm), a violet range of about 380 to about 450 nm, a blue range of about 450 to about 485 nm, a cyan range of about 485 to 500 nm, a green range of about 500 to 565 nm, a yellow range of about 565 to about 590 nm, an orange range of about 590 to 625 nm, a red range of about 625 to about 740 nm, or a near-infrared range of about 700 nm to about 1100 nm. (Item 36) Item 34. The method of item 33, wherein the first wavelength is in one of the ranges and the second wavelength is in a different one of the ranges. (Item 37) obtaining, with the image sensor, a third image data set from the sample illuminated with a third wavelength using a third set of illumination conditions; 2. The method of claim 1, wherein the third data set is combined with the first image data set and the second image data set to generate the computationally reconstructed image. (Item 38) Item 38. The method of item 37, wherein the third set of lighting conditions comprises a third number of lighting conditions that is less than the first number of lighting conditions. (Item 39) further comprising using the image sensor to obtain N additional image data sets from the sample illuminated with N additional wavelengths using N additional sets of illumination conditions; the N additional data sets are combined with the first image data set, the second image data set, and the third image data set to generate the computationally reconstructed image; Item 38. The method of item 37, wherein N comprises an integer of at least 1. (Item 40) Item 39. The method of item 39, wherein N comprises an integer in the range of about 10 to 100. (Item 41) 40. The method of claim 39, wherein the computationally reconstructed image comprises a hyperspectral image. (Item 42) Item 10. The method of item 1, wherein the computationally reconstructed image comprises one or more of a 2D image, a 3D image, a 2D intensity image, a 3D intensity image, a 2D phase image, a 3D phase image, a 2D fluorescence image, a 3D fluorescence image, a 2D hyperspectral image, or a 3D hyperspectral image. (Item 43) The first data set and the second data set correspond to a first depth of the sample, and the method further comprises: adjusting the focus of the microscope to image the sample at multiple depths; repeating the obtaining and combining steps to generate the computationally reconstructed image, the computationally reconstructed image comprising a plurality of computationally reconstructed images at different depths corresponding to the plurality of depths; The method according to item 1, comprising: (Item 44) Item 10. The method of item 1, further comprising determining the first wavelength. (Item 45) Item 45. The method of item 44, wherein the first wavelength is user-defined. (Item 46) Determining the first wavelength further comprises: using the image sensor to obtain an initial image dataset of the sample illuminated with a plurality of wavelengths; determining that a first image of the initial image data set contains more information than a second image of the initial image data set; selecting a first wavelength of the plurality of wavelengths corresponding to the first image as the first wavelength; selecting, as the second wavelength, a second wavelength of the plurality of wavelengths corresponding to the second image; Item 45. The method according to Item 44, comprising: (Item 47) Item 47. The method of item 46, wherein the information comprises spatial frequency information. (Item 48) Item 47. The method of item 46, further comprising determining the first set of lighting conditions based on information identified from the initial image dataset. (Item 49) Item 49. The method of item 48, wherein determining the first set of lighting conditions includes determining one or more of a number of light sources, a location of a light source, a combination of locations of multiple light sources, an angle of illumination, a combination of angles of illumination, a number of lights, a position of a diffuser, a light pattern, a filter, a mask, or a focus of the sample. (Item 50) Item 47. The method of item 46, further comprising determining a computational process for reconstructing the first image based on the initial image dataset. (Item 51) Item 10. The method of item 1, wherein the computationally reconstructed image comprises a three-dimensional image. (Item 52) Item 10. The method of item 1, wherein the second set of lighting conditions is different from the first set of lighting conditions. (Item 53) Item 10. The method of item 1, wherein the second image dataset is smaller than the first image dataset. (Item 54) Item 10. The method of item 1, wherein the first image dataset is obtained after the second image dataset. (Item 55) 1. A microscope for image reconstruction, comprising: an illumination source configured to illuminate the sample with multiple wavelengths at multiple angles; an image sensor; an objective lens for imaging a sample illuminated with the illumination assembly onto the image sensor; a processor operatively coupled to the illumination assembly and the image sensor, the processor configured with instructions to perform the method of any one of the preceding items; A microscope equipped with: (Item 56) Item 56. The microscope according to Item 55, wherein the plurality of wavelengths comprises one or more of a violet wavelength in a range of about 380 to about 450 nanometers (nm), a blue wavelength in a range of about 450 to about 485 nm, a cyan wavelength in a range of about 485 to 500 nm, a green wavelength in a range of about 500 to 565 nm, a yellow wavelength in a range of about 565 to about 590 nm, an orange wavelength in a range of about 590 to 625 nm, a red wavelength in a range of about 625 to about 740 nm, an infrared wavelength greater than 700 nm, or a near-infrared wavelength in a range of about 700 nm to about 1100 nm. (Item 57) Item 56. The microscope of item 55, wherein the illumination assembly is configured to illuminate the sample with multiple light sources at multiple locations corresponding to different illumination angles of the sample. (Item 58) Item 56. The microscope of item 55, further comprising a focus actuator coupled to the processor for adjusting the depth of the sample used to form an image of the sample on the image sensor. (Item 59) Item 59. The microscope of item 58, wherein the focus actuator is configured to move to a first configuration for imaging the sample at a first depth and to move to a second configuration for imaging the sample at a second depth. [Brief explanation of the drawings]

[0009] [Figure 1] FIG. 1 shows a schematic diagram of an exemplary microscope, according to some embodiments.

[0010] [Figure 2A] FIG. 2A shows a schematic diagram of the optical paths of two beam pairs when the microscope of FIG. 1 is out of focus, according to some embodiments.

[0011] [Figure 2B] FIG. 2B shows a schematic diagram of the optical paths of two beam pairs when the microscope of FIG. 1 is in focus, according to some embodiments.

[0012] [Figure 3] FIG. 3 shows a flowchart of an exemplary process for compressed acquisition of microscopic images, according to some embodiments.

[0013] [Figure 4] 4A-C show several workflow diagrams for an exemplary process for compressed acquisition of microscopy images, according to some embodiments.

[0014] [Figure 5] FIG. 5 shows a workflow diagram for an exemplary process for compressed acquisition of microscopy images, according to some embodiments.

[0015] [Figure 6] FIG. 6 shows a high-resolution reconstruction of a sample from multiple low-resolution images in a first channel according to some embodiments.

[0016] [Figure 7] FIG. 7 shows a single image of the sample of FIG. 6 acquired in a second channel, according to some embodiments.

[0017] [Figure 8] FIG. 8 shows a single image of the sample of FIG. 6 acquired in a third channel, according to some embodiments.

[0018] [Figure 9] FIG. 9 shows a color high-resolution image generated by processing the images of FIGS. 6, 7, and 8 together, according to some embodiments.

[0019] [Figure 10] FIG. 10 shows a zoomed-in raw image and corresponding cell structure obtained with an image sensor using a red illumination color.

[0020] [Figure 11] FIG. 11 shows a zoomed-in raw image obtained with an image sensor using blue illumination color and the corresponding cell structure of FIG.

[0021] [Figure 12] FIG. 12 shows a zoomed-in raw image obtained with an image sensor using a green illumination color and the corresponding cell structure of FIG.

[0022] [Figure 13]FIG. 13 shows a computationally reconstructed image obtained from multiple images illuminated with green light and the corresponding cell structure of FIGS. 10-12.

[0023] [Figure 14] FIG. 14 shows a zoomed-in computationally reconstructed color image and corresponding cellular structure from images such as those in FIGS. 10-12. DETAILED DESCRIPTION OF THE INVENTION

[0024] (Detailed explanation) The following detailed description provides a deeper understanding of the features and advantages of the inventions described in this disclosure, in accordance with the embodiments disclosed herein. While the detailed description includes many specific embodiments, these are provided by way of example only and should not be construed as limiting the scope of the inventions disclosed herein.

[0025] Because cone cells in the human eye can produce maximum visual acuity at approximately 555 nm, in certain lighting conditions, the highest human-perceivable resolution may be the green channel. For other color channels, the human eye may not perceive resolution as high as the green channel. In some embodiments, computationally reconstructed images comprise a higher spatial frequency bandwidth for a first wavelength, e.g., green, and a lower spatial frequency bandwidth for a second wavelength, e.g., red. A user may perceive these images as having the higher spatial frequency bandwidth of the first wavelength while perceiving the images as color images. While color images are referenced, this approach can also be applied to computationally reconstructed images with only two wavelengths, which may comprise ultraviolet or infrared wavelengths. The embodiments disclosed herein may improve the imaging time (e.g., acquisition time and reconstruction time) of a computational imaging system by processing images in a manner tailored to different color channels (e.g., wavelengths). This approach may reduce the number of images acquired and / or used for reconstruction. Additionally, different reconstruction processes may be used for different wavelengths.

[0026] In some embodiments, the number of low-resolution images obtained may vary for different wavelengths based on the sensitivity of the human eye to different wavelengths. For example, in one embodiment, a system described herein may perform computational imaging using a green channel, a red channel, and a blue channel. Due to the sensitivity of the human eye to the green channel, a system described herein may obtain multiple images in the green channel and fewer images (such as a single image) in each of the red and blue channels without significantly degrading human perception of the resulting reconstructed image. In some embodiments, a system described herein may obtain multiple images in the green channel for reconstruction of a high-resolution computational image in the green channel, while obtaining fewer images in the red and blue channels for reconstruction of a medium-resolution image in the red and blue channels.

[0027] Slides of biological and other samples may retain more information in some color channels than others. For example, capturing high-resolution detail using the green channel may be sufficient to capture information. When certain dyes or other properties are present, there may be benefits to capturing high resolution in other channels instead of, or in addition to, the green channel. This approach may also be applied to scenarios using other wavelengths (e.g., infrared (IR), ultraviolet (UV), and / or fluorescent wavelengths).

[0028] The following will provide a detailed description of adaptive sensing with reference to Figures 1-9. Figures 1 and 2 illustrate a microscope and various microscope configurations. Figures 3-5 illustrate an exemplary process for compressed acquisition of a microscope image of a sample. Figures 6-9 illustrate exemplary images of a sample at different wavelengths.

[0029] FIG. 1 is a diagrammatic representation of a microscope 100 consistent with exemplary disclosed embodiments. As used herein, the term “microscope” generally refers to any device or instrument for magnifying objects smaller than those readily observable by the naked eye, i.e., creating an image for a user of an object where the image is larger than the object. One type of microscope may be an “optical microscope,” which uses light in combination with an optical system to magnify the object. An optical microscope may be a single microscope with one or more magnifying lenses. Another type of microscope may be a “computational microscope,” which includes an image sensor and image processing algorithms to enhance or magnify the size or other properties of an object. Computational microscopes may be dedicated devices or may be created by incorporating software and / or hardware into an existing optical microscope to generate high-resolution digital images. As shown in FIG. 1 , the microscope 100 includes an image capture device 102, a focus actuator 104, a controller 106 connected to a memory 108, an illumination assembly 110, and a user interface 112. An exemplary use of the microscope 100 may be to capture an image of a sample 114 mounted on a stage 116 located within the field of view (FOV) of the image capture device 102, process the captured image, and present a magnified image of the sample 114 on the user interface 112.

[0030] The image capture device 102 may be used to capture images of the sample 114. As used herein, the term “image capture device” generally refers to a sensor or device that records an optical signal incident on a lens as an image or a sequence of images. The optical signal may be in the near-infrared, infrared, visible, and ultraviolet spectrum. Examples of image capture devices include CCD cameras, CMOS cameras, photosensor arrays, video cameras, camera-equipped mobile phones, webcams, preview cameras, microscope objective lenses and detectors, etc. Some embodiments may include only a single image capture device 102, while other embodiments may include two, three, or even four or more image capture devices 102. In some embodiments, the image capture device 102 may be configured to capture images within a defined field of view (FOV). Also, when the microscope 100 includes several image capture devices 102, the image capture devices 102 may have overlapping areas within their respective FOVs. Image capture device 102 may have one or more image sensors (not shown in FIG. 1 ) for capturing image data of sample 114. In other embodiments, image capture device 102 may be configured to capture images at an image resolution greater than VGA, greater than 1 megapixel, greater than 2 megapixels, greater than 5 megapixels, greater than 10 megapixels, greater than 12 megapixels, greater than 15 megapixels, or greater than 20 megapixels. Additionally, image capture device 102 may also be configured to have a pixel size less than 15 micrometers, less than 10 micrometers, less than 5 micrometers, less than 3 micrometers, or less than 1.6 micrometers.

[0031] In some embodiments, the microscope 100 includes a focus actuator 104. As used herein, the term “focus actuator” generally refers to any device capable of converting an input signal into physical motion for adjusting the relative distance between the sample 114 and the image capture device 102. Various focus actuators may be used, including, for example, linear motors, electrostrictive actuators, electrostatic motors, capacitive motors, voice coil actuators, magnetostrictive actuators, etc. In some embodiments, the focus actuator 104 may include an analog position feedback sensor and / or a digital position feedback element. The focus actuator 104 is configured to receive commands from the controller 106 to focus the light beam and form a clear, sharply defined image of the sample 114. In the example illustrated in FIG. 1 , the focus actuator 104 may be configured to adjust the distance by moving the image capture device 102. In some examples, the focus actuator 104 may be configured to adjust the depth of the sample 114 used to form an image of the sample 114 on the image capture device 102. For example, the focus actuator 114 may be configured to move to a first configuration for imaging or capturing the sample 114 at a first depth and to move to a second configuration for imaging or capturing the sample 114 at a second depth.

[0032] However, in other embodiments, the focus actuator 104 may be configured to adjust the distance by moving the stage 116, or by moving both the image capture device 102 and the stage 116. The microscope 100 may also include a controller 106 for controlling the operation of the microscope 100 according to disclosed embodiments. The controller 106 may include various types of devices for performing logical operations on one or more inputs of image data and other data according to stored or accessible software instructions that provide the desired functionality. For example, the controller 106 may include a central processing unit (CPU), support circuits, a digital signal processor, an integrated circuit, cache memory, or any other type of device for image processing and analysis, such as a graphics processing unit (GPU). The CPU may include any number of microcontrollers or microprocessors configured to process imagery from the image sensor. For example, the CPU may include any type of single or multi-core processor, mobile device microcontroller, etc. Various processors may be used, including, for example, processors available from manufacturers such as Intel®, AMD®, etc., and may include various architectures (e.g., x86 processor, ARM®, etc.). The support circuits may generally be any number of circuits known in the art, including cache, power supplies, clocks, and input / output circuits. The controller 106 may be in a remote location, such as a computing device that is communicatively coupled to the microscope 100.

[0033] In some embodiments, the controller 106 may be associated with memory 108 that is used to store software that, when executed by the controller 106, controls the operation of the microscope 100. In addition, the memory 108 may also store electronic data associated with the operation of the microscope 100, such as, for example, captured or generated images of the sample 114. In one case, the memory 108 may be integrated into the controller 106. In another case, the memory 108 may be separate from the controller 106.

[0034] Specifically, memory 108 may refer to multiple structures or computer-readable storage media located on controller 106 or at a remote location, such as a cloud server. Memory 108 may comprise any number of random access memory, read-only memory, flash memory, disk drives, optical storage devices, tape storage devices, removable storage devices, and other types of storage devices.

[0035] The microscope 100 may include an illumination assembly 110. As used herein, the term "illumination assembly" generally refers to any device or system capable of projecting light to illuminate a sample 114.

[0036] The illumination assembly 110 may include any number of light sources, such as light-emitting diodes (LEDs), LED arrays, lasers, and lamps configured to emit light, such as halogen lamps, incandescent lamps, or sodium lamps. In one embodiment, the illumination assembly 110 may include only a single light source. Alternatively, the illumination assembly 110 may include 4, 16, or even more than 100 light sources organized in an array or matrix. In some embodiments, the illumination assembly 110 may use one or more light sources located on a parallel surface to illuminate the sample 114. In other embodiments, the illumination assembly 110 may use one or more light sources located on a surface perpendicular to the sample 114 or at an angle thereto.

[0037] Additionally, the illumination assembly 110 may be configured to illuminate the sample 114 under a series of different illumination conditions. In one example, the illumination assembly 110 may include multiple light sources arranged at different illumination angles, such as a two-dimensional array of light sources. In this case, the different illumination conditions may include different illumination angles. For example, FIG. 1 depicts a beam 118 projected from a first illumination angle α1 and a beam 120 projected from a second illumination angle α2. In some embodiments, the first illumination angle α1 and the second illumination angle α2 may have the same value but opposite signs. In other embodiments, the first illumination angle α1 may be distinct from the second illumination angle α2. However, both angles originate from a point within the acceptance angle of the optical system.

[0038] In another embodiment, the illumination assembly 110 may include one or more light sources configured to emit light at different wavelengths. In this case, the different illumination conditions may include different wavelengths. The different wavelengths may include one or more of a violet wavelength in the range of about 380 to about 450 nanometers (nm), a blue wavelength in the range of about 450 to about 485 nm, a cyan wavelength in the range of about 485 to 500 nm, a green wavelength in the range of about 500 to 565 nm, a yellow wavelength in the range of about 565 to about 590 nm, an orange wavelength in the range of about 590 to 625 nm, a red wavelength in the range of about 625 to about 740 nm, an infrared wavelength greater than 700 nm, or a near-infrared wavelength in the range of about 700 nm to about 1100 nm.

[0039] In yet another embodiment, the lighting assembly 110 may be configured to use several light sources at a given time. In this case, different lighting conditions may comprise different lighting patterns. Thus, consistent with the present disclosure, different lighting conditions may be selected from a group including different durations, different intensities, different positions, different lighting angles, different lighting patterns, different wavelengths, or any combination thereof.

[0040] Consistent with disclosed embodiments, the microscope 100 may include, be connected to, or communicate with (e.g., via a network or wirelessly, e.g., via Bluetooth®) a user interface 112. The term “user interface” as used herein generally refers to any device suitable for presenting a magnified image of the sample 114 or for receiving input from one or more users of the microscope 100. FIG. 1 illustrates two examples of the user interface 112. The first example is a smartphone or tablet that communicates wirelessly with the controller 106 via Bluetooth®, a cellular connection, or a Wi-Fi connection, either directly or through a remote server. The second example is a PC display physically connected to the controller 106. In some embodiments, the user interface 112 may include user output devices, including, for example, a display, a haptic device, a speaker, etc. In other embodiments, the user interface 112 may include user input devices, including, for example, a touchscreen, a microphone, a keyboard, a pointer device, a camera, a knob, a button, etc. Using such input devices, a user may be able to provide information input or commands to microscope 100 by typing instructions or information, providing voice commands, selecting on-screen menu options using buttons, a pointer, or eye-tracking capabilities, or through any other suitable technique for communicating information to microscope 100. User interface 112 may be coupled (physically or wirelessly) to one or more processing devices, such as controller 106, to provide and receive information to or from a user and process that information. In some embodiments, such processing devices may execute instructions for responding to keyboard strokes or menu selections, recognizing and interpreting touches and / or gestures made on a touchscreen, recognizing and tracking eye movements, receiving and interpreting voice commands, etc.

[0041] The microscope 100 may also include or be connected to a stage 116. The stage 116 comprises any horizontal, rigid surface on which the sample 114 can be mounted for inspection. The stage 116 may include a mechanical connector for holding the slide containing the sample 114 in a fixed position. The mechanical connector may use one or more of the following: a mount, a mounting member, a holding arm, a clamp, a clip, an adjustable frame, a locking mechanism, a spring, or any combination thereof. In some embodiments, the stage 116 may include a translucent portion or opening to allow light to illuminate the sample 114. For example, light transmitted from the illumination assembly 110 may pass through the sample 114 and toward the image capture device 102. In some embodiments, the stage 116 and / or the sample 114 may be moved using motors or manual control in the XY plane to enable imaging of multiple areas of the sample.

[0042] 2A and 2B depict close-up views of microscope 100 in two cases. Specifically, FIG. 2A illustrates the optical paths of the two beam pairs when microscope 100 is out of focus, and FIG. 2B illustrates the optical paths of the two beam pairs when microscope 100 is in focus. In cases where the sample is thicker than the depth of focus or where the depth changes rapidly, some portions of the sample may be in focus while other portions may be out of focus.

[0043] As shown in FIGS. 2A and 2B , the image capture device 102 includes an image sensor 200 and a lens 202. In microscopy, the lens 202 may be referred to as the objective lens of the microscope 100. The term “image sensor” as used herein generally refers to a device capable of detecting and converting optical signals into electrical signals. The electrical signals may be used to form an image or video stream based on the detected signals. Examples of the image sensor 200 may include a semiconductor charge-coupled device (CCD), an active pixel sensor in a complementary metal-oxide semiconductor (CMOS), or an N-type metal-oxide semiconductor (NMOS, Live MOS). The term “lens” as used herein refers to a polished or shaped piece of glass, plastic, or other transparent material with opposing surfaces, one or both of which are curved, through which light rays are refracted so that they converge or diverge to form an image. The term “lens” may also refer to an element containing one or more lenses as defined above, such as in a microscope objective. The lens is positioned at least approximately transverse to the optical axis of the image sensor 200. The lens 202 may be used to collect light beams from the sample 114 and direct them towards the image sensor 200. In some embodiments, the image capture device 102 may comprise a fixed lens or a zoom lens.

[0044] When the sample 114 is located at the focal plane 204, the image projected from the lens 202 is perfectly focused. The term "focal plane" is used herein to describe a plane that is perpendicular to the optical axis of the lens 202 and passes through the focal point of the lens. The distance between the focal plane 204 and the center of the lens 202 is called the focal length and is represented by D1. In some cases, the sample 114 may not be perfectly flat, and small differences may exist between the focal plane 204 and various regions of the sample 114. Therefore, the distance between the focal plane 204 and the sample 114 or a region of interest (ROI) of the sample 114 is marked as D2. The distance D2 corresponds to the extent to which the image of the sample 114 or the image of the ROI of the sample 114 is out of focus. For example, the distance D2 may be between 0 and approximately 3 mm. In some embodiments, D2 may be greater than 3 mm. When the distance D2 is equal to zero, the image of the sample 114 (or the image of the ROI of the sample 114) is perfectly focused. In contrast, when D2 has a non-zero value, the image of the sample 114 (or the image of the ROI of the sample 114) is out of focus.

[0045] FIG. 2A depicts a case where an image of sample 114 is out of focus. For example, an image of sample 114 may be out of focus when the beams of light received from sample 114 do not converge on image sensor 200. FIG. 2A depicts beam pair 206 and beam pair 208. Neither pair converges on image sensor 200. For simplicity, the light path below sample 114 is not shown. Consistent with this disclosure, beam pair 206 may correspond to beam 120 projected from illumination assembly 110 at illumination angle α2, and beam pair 208 may correspond to beam 118 projected from illumination assembly 110 at illumination angle α1. Additionally, beam pair 206 may strike image sensor 200 in parallel with beam pair 208. The term "concurrently" in this context means that image sensor 200 recorded information associated with two or more beam pairs during coincident or overlapping time periods, either where one begins and ends during the duration of the other, or where the later one begins before the completion of the other. In other embodiments, beam pair 206 and beam pair 208 may contact image sensor 200 sequentially. The term "concurrently" means that image sensor 200 began recording information associated with, for example, beam pair 206 after completing recording information associated with beam pair 208.

[0046] As discussed above, D2 is the distance between the focal plane 204 and the sample 114, which corresponds to the extent to which the sample 114 is out of focus. In one example, D2 may have a value of 50 micrometers. The focus actuator 104 is configured to vary the distance D2 by converting an input signal from the controller 106 into physical movement. In some embodiments, the focus actuator 104 may move the image capture device 102 to focus the image of the sample 114. In this example, the focus actuator 104 may move the image capture device 102 up 50 micrometers to focus the image of the sample 114. In other embodiments, the focus actuator 104 may move the stage 116 down to focus the image of the sample 114. Thus, in this example, instead of moving the image capture device 102 up 50 micrometers, the focus actuator 104 may move the stage 116 down 50 micrometers.

[0047] 2B illustrates a case where the image of sample 114 is in focus. In this case, beam pairs 206 and 208 both converge onto image sensor 200, and distance D2 is equal to zero. In other words, focusing the image of sample 114 (or the image of the ROI of sample 114) may depend on adjusting the relative distance between image capture device 102 and sample 114. The relative distance may be represented by D1-D2, and when distance D2 is equal to zero, the relative distance between image capture device 102 and sample 114 is equal to distance D1, which means that the image of sample 114 is in focus.

[0048] 3 illustrates an exemplary method 300 for compressed acquisition of a microscopic image of a sample, such as sample 114, using a suitable microscope, such as microscope 100. In one embodiment, each of the steps illustrated in FIG. 3 may represent an algorithm whose structure includes and / or is represented by multiple sub-steps, examples of which will be provided in more detail below.

[0049] 3, in step 310, one or more of the systems described herein may use an image sensor to obtain a first image dataset from a sample illuminated using a first set of illumination conditions comprising a first number of illumination conditions, each comprising a first wavelength. For example, microscope 100 may use image capture device 102 to obtain a first image dataset from sample 114 illuminated by illumination assembly 110 using a first set of illumination conditions having a first number of illumination conditions. The first image dataset may include multiple images of sample 114.

[0050] The first set of illumination conditions may each include a first wavelength such that the illumination assembly 110 may illuminate the sample 114 by emitting light at the first wavelength. The first wavelength may correspond to a first color. For example, the first wavelength may correspond to one of the following ranges: an ultraviolet range of about 200 to about 380 nanometers (nm), a violet range of about 380 to about 450 nm, a blue range of about 450 to about 485 nm, a cyan range of about 485 to 500 nm, a green range of about 500 to 565 nm, a yellow range of about 565 to about 590 nm, an orange range of about 590 to 625 nm, a red range of about 625 to about 740 nm, or a near-infrared range of about 700 nm to about 1100 nm. In some embodiments, the first wavelength may correspond to a first peak of a first illumination source emitting a first distribution of wavelengths. The first distribution of wavelengths may include a first full width half maximum.

[0051] In some implementations, the method 300 may include determining a first wavelength. The first wavelength may be user-defined, such as defined with and / or associated with each sample. Alternatively, determining the first wavelength may be dynamic and / or adaptive for each wavelength to be captured by the microscope 100. Determining the first wavelength may include obtaining an initial image dataset of the sample 114 illuminated with multiple wavelengths using the image capture device 102, for example, via the illumination assembly 110. The controller 106, as part of the microscope 100, may determine that a first image of the initial image dataset contains more information than a second image of the initial dataset. The microscope 100 may select the wavelength corresponding to the first image as the first wavelength. For example, the microscope 100 may capture a first image of the sample 114 illuminated under a green wavelength, a second image of the sample 114 illuminated under a red wavelength, and a third image of the sample 114 illuminated under a blue wavelength. The microscope 100 may select as the first wavelength a wavelength corresponding to an image with the most information, which may correspond to spatial frequency information. For example, if the green channel image contains more information than the red channel image and the blue channel image, the microscope 100 may select green as the first wavelength. The microscope 100 may also prioritize or otherwise rank other wavelengths. For example, the microscope 100 may select red as the second wavelength and blue as the third wavelength.

[0052] In some embodiments, the microscope 100 may determine a first set of lighting conditions. The microscope 100 may determine the first set of lighting conditions based on information identified from the initial image data. The microscope 100 may determine, for example, the number of light sources, the location of the light sources, a combination of light source locations, an illumination angle, a combination of illumination angles, the number of lights, the position of a diffuser, a light pattern, a filter, a mask, or a focus point on the sample.

[0053] In some embodiments, the microscope 100 may determine a computational process for reconstructing the first image. For example, the microscope 100 may determine the computational process based on the initial image dataset, such as by recognizing certain information and / or patterns from the initial image dataset.

[0054] At step 320, one or more of the systems described herein may use an image sensor to obtain a second image dataset from a sample illuminated using a second set of illumination conditions comprising a second number of illumination conditions, each comprising a second wavelength. The first number of illumination conditions may be greater than the second number of illumination conditions. For example, the microscope 100 may use the image capture device 102 to obtain a second image dataset from a sample 114 illuminated by the illumination assembly 110 using a second set of illumination conditions having a second number of illumination conditions less than the first number of illumination conditions. The second image dataset may include one or more images of the sample 114.

[0055] The first number may be at least two times greater than the second number. A second acquisition time associated with the second image data set may be less than or equal to half the first acquisition time associated with the first image data set.

[0056] In addition to having fewer lighting conditions, the second set of lighting conditions may also differ from the first set of lighting conditions. The second image data set may be smaller than the first image data set. For example, because fewer lighting conditions exist, the microscope 100 may capture fewer images for the second image data set than the first image data set. In some implementations, the microscope 100 may acquire the second image data set after acquiring the first image data set.

[0057] The second set of illumination conditions may each include a second wavelength such that the illumination assembly 110 may illuminate the sample 114 by emitting light at the second wavelength. The second wavelength may correspond to a second color. For example, the second wavelength may correspond to one of the following ranges: an ultraviolet range of about 200 to about 380 nanometers (nm), a violet range of about 380 to about 450 nm, a blue range of about 450 to about 485 nm, a cyan range of about 485 to 500 nm, a green range of about 500 to 565 nm, a yellow range of about 565 to about 590 nm, an orange range of about 590 to 625 nm, a red range of about 625 to about 740 nm, or a near-infrared range of about 700 nm to about 1100 nm.

[0058] The second wavelength may be different from the first wavelength. For example, the second wavelength may correspond to a second color different from the first color (of the first wavelength). In some embodiments, the first wavelength may correspond to a first peak of a first illumination source emitting a first distribution of wavelengths (which may include a first full-width half-maximum), and the second wavelength may correspond to a second peak of the second distribution of wavelengths (which may include a second full-width half-maximum). The first full-width half-maximum may not overlap with the second full-width half-maximum. Alternatively, the first and second wavelengths may correspond to different ranges of wavelengths.

[0059] In some embodiments, the second wavelength may be determined. For example, the second wavelength may be user-defined. Alternatively, microscope 100 may determine the second wavelength as described above with respect to step 310. In addition, microscope 100 may determine a second set of lighting conditions based on the initial image data set, for example, as described above with respect to step 310.

[0060] In step 330, one or more of the systems described herein may combine the first image data set and the second image data set into a computationally reconstructed image of the sample. For example, microscope 100 may combine the first image data set and the second image data set into a computationally reconstructed image of sample 114.

[0061] The computationally reconstructed image may include one or more of a two-dimensional (2D) image, a three-dimensional (3D) image, a 2D intensity image, a 3D intensity image, a 2D phase image, a 3D phase image, a 2D fluorescence image, a 3D fluorescence image, a 2D hyperspectral image, or a 3D hyperspectral image. The computationally reconstructed image may include a color image. For example, the color image may include two or more of a red channel, a green channel, or a blue channel. In such an example, a first wavelength may correspond to one of the red channel, the green channel, or the blue channel, and a second wavelength may correspond to another of the red channel, the blue channel, or the green channel.

[0062] Additionally, the spatial frequency bandwidth of the computationally reconstructed image may exceed the spatial frequency bandwidth of each of the plurality of images obtained using the first set of lighting conditions by a factor of at least 1.5.

[0063] In some embodiments, the microscope 100 may process the first image dataset and the second image dataset separately to generate a first computationally reconstructed image from the first image dataset and a second computationally reconstructed image from the second dataset. The microscope 100 may combine the first computationally reconstructed image with the second computationally reconstructed image to generate the computationally reconstructed image. The first image dataset may include a first plurality of images, and the second image dataset may include one or more images. The one or more images may include a single image obtained using a second set of lighting conditions. Alternatively, the one or more images may include a second plurality of images. The microscope 100 may process the first plurality of images and the one or more images together to generate the computationally reconstructed image.

[0064] Additionally, in some embodiments, microscope 100 may use an image sensor (e.g., image capture device 102) to obtain a third image data set from a sample illuminated with a third wavelength using a third set of illumination conditions. The third set of illumination conditions may include a third number of illumination conditions less than the first number of illumination conditions. Microscope 100 may combine the third data set with the first and second image data sets to generate a computationally reconstructed image.

[0065] In another example, microscope 100 may use an image sensor (e.g., image capture device 102) to obtain N additional image data sets from a sample illuminated with N additional wavelengths using N additional sets of illumination conditions. N may be an integer of at least 1. For example, N may be an integer in the range of approximately 10 to 100. Microscope 100 may combine the N additional data sets with the first image data set, the second image data set, and the third image data set to generate a computationally reconstructed image. The computationally reconstructed image may include a hyperspectral image.

[0066] 4A illustrates a workflow diagram of a corresponding method 400 that may be performed by a suitable microscope, such as microscope 100. Method 400 may correspond to a variation of method 300.

[0067] 4A, in step 410 (which may correspond to step 310), microscope 100 may use image capture device 102 to obtain N1 images from channel #1 as illuminated by illumination assembly 110. In step 411 (which may correspond to step 330), microscope 100 may process the N1 images from channel #1, for example, to generate a first computationally reconstructed image for channel #1. Microscope 100 may determine a resolution for the first computationally reconstructed image based on the number of low-resolution images (e.g., N1).

[0068] In step 420 (which may correspond to step 320), microscope 100 may use image capture device 101 to obtain N2 images from channel #2 as illuminated by illumination assembly 110. In step 421 (which may correspond to step 330), microscope 100 may process the N2 images from channel #2, for example, to generate a second computationally reconstructed image for channel #2. Microscope 100 may determine a resolution for the second computationally reconstructed image based on the number of low-resolution images (e.g., N2).

[0069] Microscope 100 may repeat the acquisition and processing for each channel, e.g., channel #1-channel #M, in any order. Additionally, as described herein, microscope 100 may adaptively determine the illumination conditions, including the number of illuminations, for each channel and for each sample. In step 430 (which may correspond to step 320), microscope 100 uses image capture device 101 to acquire and process N images from channels #M through #M. M In step 431 (which may correspond to step 330), the microscope 100 may, for example, obtain N images from channel #M to generate a computationally reconstructed image for channel #M. M The images may be processed.

[0070] In step 440 (which may correspond to step 330), microscope 100 may fuse the channels (e.g., channels 1-M) to form a final image. For example, microscope 100 may use acquired images per channel, computationally reconstructed images per channel, and / or any subcombination thereof to generate a final computationally reconstructed image of sample 114. The fusion of channels can be performed in many ways. In some embodiments, one or more of steps 540, 440, 442, or 444 may include color calibration, e.g., using a color space such as CIELAB, CIELUV, YCbCr, XYZ, or CIEUVW.

[0071] 4B illustrates a workflow diagram of another method 402 that may be performed by a suitable microscope, such as microscope 100. Method 402 may correspond to a variation of method 300 and / or method 400.

[0072] 4B, in step 412 (which may correspond to step 310), microscope 100 may use image capture device 102 to obtain N1 images from channel #1 as illuminated by illumination assembly 110. In step 422 (which may correspond to step 320), microscope 100 may use image capture device 101 to obtain N2 images from channel #2 as illuminated by illumination assembly 110.

[0073] In step 413 (which may correspond to step 330), the microscope 100 may process, for example, N1 images from channel #1 and N2 images from channel #2 to generate computationally reconstructed images for channel #1 and channel #2.

[0074] The microscope 100 may repeat the acquisition and processing for each channel, e.g., channel #1-channel #M, in any order. In step 432 (which may correspond to step 320), the microscope 100 uses the image capture device 101 to acquire and process N images from channels #M through #M. M In step 433 (which may correspond to step 330), the microscope 100 may, for example, obtain N images from channel #M to generate a computationally reconstructed image for channel #M. M The images may be processed.

[0075] In step 442 (which may correspond to step 330), microscope 100 may fuse the channels (e.g., channels #1-M) to form a final image. For example, microscope 100 may use the acquired images for each channel, the computationally reconstructed images for each channel and / or channel combination, and / or any subcombination thereof to generate a final computationally reconstructed image of sample 114.

[0076] 4C illustrates a workflow diagram of another method 404 that may be performed by a suitable microscope, such as microscope 100. Method 404 may correspond to variations of method 300, method 400, and / or method 402.

[0077] Microscope 100 may acquire images and process each channel, e.g., channel #1-channel #M, in any order. As shown in FIG. 4C , in step 414 (which may correspond to step 310), microscope 100 may use image capture device 102 to acquire N1 images from channel #1 as illuminated by illumination assembly 110. In step 424 (which may correspond to step 320), microscope 100 may use image capture device 101 to acquire N2 images from channel #2 as illuminated by illumination assembly 110. In step 432 (which may correspond to step 320), microscope 100 may use image capture device 101 to acquire N2 images from channel #M. M Individual images may be obtained.

[0078] In step 415 (which may correspond to step 330), the microscope 100 may, for example, combine N1 images from channel #1, N2 images from channel #2, and N1 images from channel #M to generate a computationally reconstructed image for channels #1-#M. M The images may be processed.

[0079] In step 444 (which may correspond to step 330), microscope 100 may fuse the channels (e.g., channels #1-M) to form a final image. For example, microscope 100 may use the acquired images for each channel, the computationally reconstructed image for the channel combination, and / or any subcombination thereof to generate a final computationally reconstructed image for sample 114.

[0080] 5 illustrates a workflow diagram of a method 500 that may be performed by a suitable microscope, such as microscope 100. Method 500 may correspond to variations of method 300, method 400, method 402, and / or method 404. In particular, method 500 may correspond to a specific embodiment of method 400.

[0081] As illustrated in FIG. 5, in step 510 (which may correspond to step 310 and / or step 410), microscope 100 may use image capture device 102 to obtain N images using the green channel as illuminated by illumination assembly 110. As described above, the green channel may provide more information than the red or blue channels, and therefore, prioritizing the green channel may be desirable. Microscope 100 may adaptively determine N as described herein or may use a preconfigured value. In addition, microscope 100 may obtain N images from the green channel at a lower resolution, such as a resolution lower than the inherent resolution of microscope 100. In step 511 (which may correspond to step 330 and / or step 411), microscope 100 may process the N images from the green channel to reconstruct a high-resolution image from the green channel images, which may be, for example, low-resolution images. FIG. 6 shows a high-resolution image 600 that may be reconstructed from the N images from the green channel.

[0082] In step 520 (which may correspond to step 320 and / or step 420), microscope 100 may use image capture device 101 to acquire a single image from the red channel as illuminated by illumination assembly 110. Microscope 100 may acquire the red channel image at the native resolution of microscope 100. In step 521 (which may correspond to step 330 and / or step 421), microscope 100 may process the image from the red channel, for example, to denoise or otherwise enhance the red image. FIG. 7 shows image 700, which may be a single image acquired using Kohler illumination. As seen in FIGS. 6 and 7, image 700 may have a lower resolution than that of image 600.

[0083] In step 530 (which may correspond to step 320 and / or step 430), microscope 100 may acquire a single image from the blue channel using image capture device 101. Microscope 100 may acquire the blue channel image at the native resolution of microscope 100. In step 531 (which may correspond to step 330 and / or step 431), microscope 100 may process the image from the blue channel, for example, to remove noise from the blue image. FIG. 8 shows image 800, which may be a single image acquired using Kohler illumination. As seen in FIGS. 6 and 8, image 800 may have a lower resolution than that of image 600.

[0084] In step 540 (which may correspond to step 330 and / or step 440), microscope 100 may fuse the channels (e.g., red, green, and blue channels) to form a high-resolution color image. For example, microscope 100 may use the acquired images per channel, the processed images per channel, and / or any subcombination thereof to generate a final computationally reconstructed image of sample 114. FIG. 9 shows image 900, which may be a color high-resolution image generated by processing images 600, 700, and 800 together. As seen in FIGS. 6-9, image 900 may have higher resolution than images 600, 700, and 800. Although FIG. 5 shows acquisition and processing in the order of green, red, and blue, microscope 100 may perform acquisition and processing per channel in any order.

[0085] The improved spatial frequency bandwidth of computationally reconstructed images may be understood with reference to the example images shown in FIGS. 10-13, according to some embodiments.

[0086] FIG. 10 shows a zoomed-in raw image 1000 and corresponding cell structure obtained with an image sensor using a red illumination color.

[0087] FIG. 11 shows a zoomed-in raw image 1100 obtained with an image sensor using a blue illumination color and the corresponding cell structure of FIG.

[0088] FIG. 12 shows a zoomed-in raw image 1200 obtained with an image sensor using a green illumination color and the corresponding cell structure of FIG.

[0089] 13 shows a computationally reconstructed image 1300 obtained from a plurality of images illuminated with green and the corresponding cellular structure of FIGS. 10-12. The plurality of images were generated using a first set of illumination conditions as described herein, and the computationally reconstructed image 1300 was obtained from the plurality of images as described herein.

[0090] FIG. 14 shows a computationally reconstructed RGB image 1400 and the corresponding cellular structure of the images in FIGS. 10-13. The computationally reconstructed color image 1400 was generated using multiple green illumination conditions, a single red illumination condition, and a single blue illumination condition. As will be understood with reference to FIGS. 10-14, the computationally reconstructed color image 1400 exhibits improved resolution of the cellular structure compared to the individual red, blue, and green images of FIGS. 10-12, respectively. The computationally reconstructed color image 1400 can be obtained by combining a computationally reconstructed image corresponding to a first wavelength and a first set of illumination conditions, e.g., image 1300, with one or more images from other illumination wavelengths, e.g., image 1000 and image 1100, as described herein. Alternatively, data from images at different wavelengths and illumination conditions can be combined to generate the computationally reconstructed color image 1400 without first generating a computationally reconstructed image corresponding to a first wavelength and a first set of illumination conditions, as described herein.

[0091] The computationally reconstructed color image can be used for cell analysis and may be shown on a user-viewable display as described herein.

[0092] In some embodiments, the first number of lighting conditions is at least two times greater than the second number of lighting conditions. A second acquisition time associated with the second image data set may be less than or equal to half the first acquisition time associated with the first image data set. A spatial frequency bandwidth of the computationally reconstructed image may exceed the spatial frequency bandwidth of each of the plurality of images acquired using the first set of lighting conditions by at least 1.5 times.

[0093] In some embodiments, the computationally reconstructed image may include one or more of increased spatial frequency bandwidth, correction for optical aberrations, or increased image contrast. Microscope 100 may transform the first image data set into a spatial frequency space, which may be mapped to spatial frequencies within the spatial frequency space based on a first set of illumination conditions, to provide an increased spatial frequency bandwidth of the computationally reconstructed image compared to a first spatial frequency bandwidth of each of the plurality of images acquired using the first set of illumination conditions. The image sensor, e.g., image capture device 102, may include a spatial frequency bandwidth, and the increased spatial frequency bandwidth of the computationally reconstructed image may exceed the spatial frequency bandwidth of the image sensor divided by the magnification of the image of the sample onto the image sensor.

[0094] In some examples, the first image data set may include a first plurality of images, each having a first spatial frequency bandwidth. The increased spatial frequency bandwidth of the computationally reconstructed images may exceed the first spatial frequency bandwidth of each of the first plurality of images. The first plurality of images may include features of the sample that are not resolved using the first spatial frequency bandwidth of each of the first plurality of images, and the computationally reconstructed images may include features of the sample that are resolved using the increased spatial frequency bandwidth of the computationally reconstructed images.

[0095] Microscope 100 may provide correction for optical aberrations by separating aberration information from sample information to reduce the effect of optical aberrations on a computationally reconstructed image. Optionally, the aberration information may include aberration spatial frequencies and phases associated with the optics used to image the sample onto the image sensor. Optionally, the sample information may include sample spatial frequencies and phases associated with the structure of the sample.

[0096] The microscope 100 may provide increased image contrast in the computationally reconstructed image by computationally amplifying high spatial frequencies in the reconstructed image to better represent the sample. The computationally reconstructed image may include increased contrast. The increased contrast may include an increased ratio of high to low spatial frequencies in the computationally reconstructed image compared to the ratio of high to low spatial frequencies in each of the multiple images of the first image dataset.

[0097] The microscope 100 may present the reconstructed image on a display, such as the user interface 112. The reconstructed image may be presented on the display using a first spatial frequency bandwidth for a first color channel corresponding to a first wavelength and a second spatial frequency bandwidth for a second color channel corresponding to a second wavelength. The first spatial frequency bandwidth may be greater than the second spatial frequency bandwidth. For example, the first wavelength may correspond to green light, such that the first color channel corresponds to the green channel. The second wavelength may correspond to red or blue light, such that the second color channel corresponds to the red or blue channel. The green channel may be presented on the display using the first spatial frequency bandwidth, and the red or blue channel may be presented on the display using the second spatial frequency bandwidth.

[0098] In some examples, microscope 100 may provide the reconstructed image on a display, e.g., user interface 112, using a first spatial frequency bandwidth and a first user-perceptible color corresponding to the first wavelength and a second spatial frequency bandwidth and a second user-perceptible color corresponding to the second wavelength. The first spatial frequency bandwidth may exceed the spatial frequency bandwidth of one or more images acquired by the image sensor using a second set of lighting conditions at the second wavelength.

[0099] In some examples, the computationally reconstructed image may include a first spatial frequency bandwidth corresponding to a first wavelength and a second spatial frequency bandwidth corresponding to a second wavelength, and the first spatial frequency bandwidth may exceed the spatial frequency bandwidth of one or more images acquired by the image sensor using a second set of illumination conditions at the second wavelength.

[0100] In some embodiments, the computationally reconstructed image may include a red channel, a green channel, and a blue channel. The second wavelength may include red light corresponding to the red channel, and the third wavelength may include blue light corresponding to a third channel. The third channel may be presented on the display using a third spatial frequency bandwidth that may be less than the first spatial frequency bandwidth.

[0101] In some examples, the image sensor, e.g., image capture device 102, may include a sensor comprising a two-dimensional array of pixels. A first color of the first image data set may correspond to a first wavelength, and a second color of the second image data set may correspond to a second wavelength. Microscope 100 may map the computationally reconstructed image into red, green, and blue channels based on the first and second wavelengths.

[0102] In some embodiments, the image sensor may include a grayscale image sensor comprising a two-dimensional array of pixels. In some embodiments, the image sensor may include a color image sensor comprising a two-dimensional array of pixels and a color filter array comprising a plurality of color filters arranged across the two-dimensional array. The first image data set may be determined based on a first wavelength and a first absorption characteristic of the color filter at the first wavelength. The second image data set may be determined based on a second wavelength and a second absorption characteristic of the color filter at the second wavelength. The microscope 100 may combine the first image data set and the second image data set according to the first absorption characteristic of the color filter at the first wavelength and the second absorption characteristic of the color filter at the second wavelength to generate a computationally reconstructed image.

[0103] In some examples, the first wavelength may be different from the second wavelength, and the portion of the first image data set and the portion of the second image data set may be acquired substantially simultaneously using one or more of the set of illumination conditions illuminating the sample when the sample is illuminated using one or more of the second set of illumination conditions.

[0104] In some embodiments, the first data set and the second data set correspond to a first depth of the sample. The microscope 100 may further adjust the focus of the microscope 100 to image the sample at multiple depths. The microscope 100 may also repeat the obtaining and combining steps (e.g., steps 310-330) to generate a computationally reconstructed image. The computationally reconstructed image may include multiple computationally reconstructed images at different depths corresponding to the multiple depths.

[0105] As discussed above in connection with exemplary methods 300, 400, 402, 404, and 500, the computational imaging systems described herein may reduce acquisition times for acquiring images at different wavelengths by customizing acquisition for each wavelength. Wavelengths that will produce more information may be prioritized (e.g., given more acquisition and / or reconstruction time) over wavelengths that will produce less information. For example, the green channel may be prioritized over the red and blue channels due to the sensitivity of the human eye to the color green. The overall imaging time for the computational imaging system may be reduced without significant detrimental effects on the resulting reconstructed image by reducing acquisition and / or reconstruction times for lower-priority wavelengths while maintaining acquisition and / or reconstruction times for otherwise prioritized wavelengths. In other words, loss of information due to reducing acquisition and / or reconstruction times for lower-priority wavelengths may be mitigated by preserving information from the prioritized wavelengths.

[0106] As described herein, the computing devices and systems described and / or illustrated herein broadly represent any type or form of computing device or system capable of executing computer-readable instructions such as those contained in the modules described herein. In their most basic configurations, these computing devices may each include at least one memory device and at least one physical processor.

[0107] The terms "memory" or "memory device" as used herein generally refer to any type or form of volatile or non-volatile storage device or medium capable of storing data and / or computer-readable instructions. In one example, a memory device may store, load, and / or maintain one or more of the modules described herein. Examples of memory devices include, but are not limited to, random access memory (RAM), read-only memory (ROM), flash memory, hard disk drives (HDDs), solid-state drives (SSDs), optical disk drives, caches, one or more variations or combinations of the same, or any other suitable storage memory.

[0108] Additionally, the terms "processor" or "physical processor" as used herein generally refer to any type or form of hardware-implemented processing unit capable of interpreting and / or executing computer-readable instructions. In one example, a physical processor may access and / or modify one or more modules stored within a memory device described above. Examples of physical processors include, but are not limited to, a microprocessor, a microcontroller, a central processing unit (CPU), a field programmable gate array (FPGA) implementing a soft-core processor, an application specific integrated circuit (ASIC), one or more portions of the same, one or more variations or combinations of the same, or any other suitable physical processor.

[0109] Although illustrated as separate elements, the method steps described and / or illustrated herein may represent parts of a single application. Additionally, in some embodiments, one or more of these steps may represent or correspond to one or more software applications or programs that, when executed by a computing device, may cause the computing device to perform one or more tasks, such as method steps.

[0110] Additionally, one or more of the devices described herein may transform data, physical devices, and / or representations of physical devices from one form to another. Additionally or alternatively, one or more of the modules listed herein may transform a processor, volatile memory, non-volatile memory, and / or any other portion of a physical computing device from one form of computing device to another form of computing device by executing on the computing device, storing data on the computing device, and / or otherwise interacting with the computing device.

[0111] The term "computer-readable medium" as used herein generally refers to any form of device, carrier, or medium capable of storing or carrying computer-readable instructions. Examples of computer-readable media include, but are not limited to, transmission-type media such as carrier waves, and non-transitory-type media such as magnetic storage media (e.g., hard disk drives, tape drives, and floppy disks), optical storage media (e.g., compact discs (CDs), digital video discs (DVDs), and BLU-RAY® discs), electronic storage media (e.g., solid-state drives and flash media), and other distributed systems.

[0112] Those skilled in the art will recognize that any process or method disclosed herein can be modified in many ways. The process parameters and sequence of steps described and / or illustrated herein are given by way of example only and can be varied as desired. For example, although the steps illustrated and / or described herein are shown or discussed in a particular order, these steps do not necessarily have to be performed in the order illustrated or discussed.

[0113] The various exemplary methods described and / or illustrated herein may also omit one or more of the steps described or illustrated herein, or may comprise additional steps in addition to those disclosed. Furthermore, the steps of any method as disclosed herein can be combined with any one or more steps of any other method as disclosed herein.

[0114] A processor as described herein can be configured to perform one or more steps of any of the methods disclosed herein. Alternatively, or in combination, a processor can be configured to combine one or more steps of one or more methods as disclosed herein.

[0115] Unless otherwise stated, the terms "connected to" and "coupled to" (and their derivatives) as used in the specification and claims shall be interpreted as allowing for both direct and indirect (i.e., via other elements or components) connections. Additionally, the terms "a" and "an" as used in the specification and claims shall be interpreted as meaning "at least one of." Finally, for ease of use, the terms "including" and "having" (and their derivatives) as used in the specification and claims shall be synonymous with and have the same meaning as the word "comprising."

[0116] A processor as disclosed herein may be configured with instructions to perform any one or more steps of any method as disclosed herein.

[0117] It should be understood that the terms "first," "second," "third," etc. may be used herein to describe various layers, elements, components, regions, or sections without referring to any particular order or sequence of events. These terms are merely used to distinguish one layer, element, component, region, or section from another layer, element, component, region, or section. A first layer, element, component, region, or section as described herein could be referred to as a second layer, element, component, region, or section without departing from the teachings of the present disclosure.

[0118] As used herein, the term "or" is used inclusively to refer to items as alternatives and in combination.

[0119] As used herein, letters such as numbers refer to similar elements.

[0120] The present disclosure includes the following numbered clauses: Each clause may be combined with one or more other clauses to the extent that such combinations are consistent with the teachings disclosed herein.

[0121] Clause 1. A method for generating a computationally reconstructed image, the method including: acquiring, with an image sensor, a first image data set from a sample illuminated using a first set of illumination conditions comprising a first number of illumination conditions, each comprising a first wavelength; acquiring, with the image sensor, a second image data set from the sample illuminated using a second set of illumination conditions comprising a second number of illumination conditions, each comprising a second wavelength, the first number being greater than the second number; and combining the first image data set and the second image data set into a computationally reconstructed image of the sample.

[0122] Clause 2. The method of Clause 1, wherein the computationally reconstructed image comprises a first spatial frequency bandwidth corresponding to a first wavelength and a second spatial frequency bandwidth corresponding to a second wavelength, the first spatial frequency bandwidth exceeding the second spatial frequency bandwidth.

[0123] Clause 3. The method of Clause 2, wherein the first spatial frequency bandwidth exceeds a spatial frequency bandwidth of one or more images obtained by the image sensor using a first set of illumination conditions at a first wavelength.

[0124] Clause 4. The method of Clause 2, wherein the second spatial frequency bandwidth exceeds the spatial frequency bandwidth of one or more images obtained by the image sensor using a second set of illumination conditions at a second wavelength.

[0125] Clause 5. The method of Clause 2, wherein the computationally reconstructed image comprises one or more of increased contrast or aberration correction for a second wavelength based, at least in part, on the first image data set from the first wavelength.

[0126] Clause 6. The method of Clause 1, further comprising: providing the reconstructed image on a display using a first spatial frequency bandwidth and a first user-perceivable color corresponding to a first wavelength and a second spatial frequency bandwidth and a second user-perceivable color corresponding to a second wavelength, wherein the first spatial frequency bandwidth exceeds the spatial frequency bandwidth of one or more images obtained by the image sensor using the second spatial frequency bandwidth and a first set of lighting conditions at the first wavelength.

[0127] Clause 7. The method of Clause 1, wherein the first number is at least two times greater than the second number, wherein a second acquisition time associated with the second image data set is less than or equal to half of the first acquisition time associated with the first image data set, and wherein a spatial frequency bandwidth of the computationally reconstructed image exceeds a spatial frequency bandwidth of each of the plurality of images acquired using the first set of illumination conditions by at least 1.5 times.

[0128] Appendix 8. The method of Appendix 1, wherein the first image dataset comprises a plurality of images of the sample, and the second image dataset comprises one or more images of the sample, the second image dataset comprising fewer images of the sample than the first image dataset.

[0129] Clause 9. The method of clause 1, wherein the computationally reconstructed image comprises one or more of increased spatial frequency bandwidth, correction for optical aberrations, or increased image contrast.

[0130] Clause 10. The method of clause 9, wherein the first image data set is transformed into a spatial frequency space and mapped to spatial frequencies within the spatial frequency space based on a first set of lighting conditions, providing an increased spatial frequency bandwidth of the computationally reconstructed image compared to a first spatial frequency bandwidth of each of the plurality of images obtained using the first set of lighting conditions.

[0131] Clause 11. The method of clause 9, wherein the image sensor has a spatial frequency bandwidth, and wherein the increased spatial frequency bandwidth of the computationally reconstructed image exceeds the spatial frequency bandwidth of the image sensor divided by the magnification of the image of the sample onto the image sensor.

[0132] Clause 12. The method of Clause 9, wherein the first image data set comprises a first plurality of images, each having a first spatial frequency bandwidth, and wherein the increased spatial frequency bandwidth of the computationally reconstructed images exceeds the first spatial frequency bandwidth of each of the first plurality of images.

[0133] Appendix 13. The method of Appendix 12, wherein the first plurality of images comprise sample features that are not resolved using a first spatial frequency bandwidth of the respective first plurality of images, and the computationally reconstructed image comprises sample features that are resolved using an increased spatial frequency bandwidth of the computationally reconstructed image.

[0134] Clause 14. The method of clause 9, wherein correction of optical aberrations is provided by separating aberration information from sample information so as to reduce the effect of optical aberrations on the computationally reconstructed image, optionally the aberration information comprising aberration spatial frequencies and phases associated with an optical system used to image the sample onto the image sensor, and optionally the sample information comprising sample spatial frequencies and phases associated with the structure of the sample.

[0135] Clause 15. The method of clause 9, wherein the increased image contrast of the computationally reconstructed image is provided by computationally amplifying high spatial frequencies of the reconstructed image to better represent the sample.

[0136] Clause 16. The method of Clause 9, wherein the computationally reconstructed image comprises increased contrast, the increased contrast comprising an increased ratio of high to low spatial frequencies in the computationally reconstructed image compared to a ratio of high to low spatial frequencies in each of the plurality of images of the first image dataset.

[0137] Appendix 17. The method of Appendix 1, wherein the first image data set and the second image data set are processed separately to generate a first computationally reconstructed image from the first image data set and a second computationally reconstructed image from the second data set, and the first computationally reconstructed image is combined with the second computationally reconstructed image to generate a computationally reconstructed image.

[0138] Appendix 18. The method of Appendix 1, wherein the first image dataset comprises a first plurality of images, and the second image dataset comprises one or more images, and the first plurality of images and the one or more images are processed together to generate a computationally reconstructed image.

[0139] Clause 19. The method of Clause 18, wherein the one or more images comprise a single image obtained using a second set of lighting conditions.

[0140] Clause 20. The method of Clause 18, wherein the one or more images comprise a second plurality of images.

[0141] Appendix 21. The method of Appendix 1, wherein the computationally reconstructed image comprises a color image, the color image comprising two or more of a red channel, a green channel, or a blue channel.

[0142] Clause 22. The method of clause 21, wherein the first wavelength corresponds to one of a red channel, a green channel, or a blue channel, and the second wavelength corresponds to another of the red channel, the blue channel, or the green channel.

[0143] Addendum 23. The method of Addendum 22, wherein the reconstructed image is shown on the display using a first spatial frequency bandwidth for a first color channel corresponding to a first wavelength and using a second spatial frequency bandwidth for a second color channel corresponding to a second wavelength, the first spatial frequency bandwidth exceeding the second spatial frequency bandwidth.

[0144] Addendum 24. The method of Addendum 23, wherein the first wavelength comprises green light, the first channel comprises a green channel, the second wavelength comprises red light or blue light, the second color channel comprises a red channel or a blue channel, and the green channel is presented on the display using a first spatial frequency bandwidth and the red channel or the blue channel is presented on the display using a second spatial frequency bandwidth.

[0145] Appendix 25. The method of Appendix 24, wherein the computationally reconstructed image comprises a red channel, a green channel, and a blue channel, the second wavelength comprises red light corresponding to the red channel, and the third wavelength comprises blue light corresponding to a third channel, the third channel being shown on the display using a third spatial frequency bandwidth that is less than the first spatial frequency bandwidth.

[0146] Addendum 26. The method of Addendum 21, wherein the image sensor comprises a sensor comprising a two-dimensional array of pixels, wherein a first color of the first image data set corresponds to a first wavelength and a second color of the second image data set corresponds to a second wavelength, and wherein the computationally reconstructed image is mapped to a red channel, a green channel, and a blue channel based on the first wavelength and the second wavelength.

[0147] Clause 27. The method of clause 26, wherein the image sensor comprises a grayscale image sensor comprising a two-dimensional array of pixels.

[0148] Addendum 28. The method of Addendum 26, wherein the image sensor comprises a color image sensor comprising a two-dimensional array of pixels, and a color filter array comprising a plurality of color filters arranged across the two-dimensional array.

[0149] Addendum 29. The method of Addendum 28, wherein the first image data set is determined based on a first wavelength and a first absorption characteristic of the color filter at the first wavelength, and the second image data set is determined based on a second wavelength and a second absorption characteristic at the second wavelength.

[0150] Addendum 30. The method of Addendum 29, wherein the first image data set and the second image data set are combined according to a first absorption characteristic of the color filter at a first wavelength and a second absorption characteristic of the color filter at a second wavelength to generate a computationally reconstructed image.

[0151] Clause 31. The method of Clause 28, wherein the first wavelength is different from the second wavelength, and wherein the portion of the first image data set and the portion of the second image data set are obtained substantially simultaneously using one or more of a set of illumination conditions illuminating the sample when the sample is illuminated using one or more of a second set of illumination conditions.

[0152] Clause 32. The method of clause 1, wherein the first wavelength is different from the second wavelength.

[0153] Clause 33. The method of Clause 32, wherein the first wavelength comprises a first color and the second wavelength comprises a second color different from the first color.

[0154] Clause 34. The method of clause 32, wherein the first wavelength comprises a first peak of a first illumination source emitting a first distribution of wavelengths, the first distribution of wavelengths comprising a first full-width half-maximum, and the second wavelength comprises a second peak of a second distribution of wavelengths, the second distribution of wavelengths comprising a second full-width half-maximum, wherein the first full-width half-maximum does not overlap with the second full-width half-maximum.

[0155] Appendix 35. The method of Appendix 32, wherein the first wavelength is within one of the following ranges and the second wavelength is within a different one of the following ranges: an ultraviolet range of about 200 to about 380 nanometers (nm), a violet range of about 380 to about 450 nm, a blue range of about 450 to about 485 nm, a cyan range of about 485 to 500 nm, a green range of about 500 to 565 nm, a yellow range of about 565 to about 590 nm, an orange range of about 590 to 625 nm, a red range of about 625 to about 740 nm, or a near-infrared range of about 700 nm to about 1100 nm.

[0156] Clause 36. The method of clause 33, wherein the first wavelength is within one of the ranges and the second wavelength is within a different one of the ranges.

[0157] Appendix 37. The method of Appendix 1, further comprising obtaining, with the image sensor, a third image data set from the sample illuminated with a third wavelength using a third set of illumination conditions, the third data set being combined with the first image data set and the second image data set to generate a computationally reconstructed image.

[0158] Clause 38. The method of clause 37, wherein the third set of lighting conditions comprises a third number of lighting conditions that is less than the first number of lighting conditions.

[0159] Clause 39. The method of Clause 37, further comprising obtaining, with the image sensor, N additional image data sets from the sample illuminated with N additional wavelengths using N additional sets of illumination conditions, the N additional data sets being combined with the first image data set, the second image data set, and the third image data set to generate a computationally reconstructed image, where N comprises an integer of at least 1.

[0160] Clause 40. The method of clause 39, wherein N comprises an integer in the range of about 10 to 100.

[0161] Clause 41. The method of Clause 39, wherein the computationally reconstructed image comprises a hyperspectral image.

[0162] Clause 42. The method of Clause 1, wherein the computationally reconstructed image comprises one or more of a 2D image, a 3D image, a 2D intensity image, a 3D intensity image, a 2D phase image, a 3D phase image, a 2D fluorescence image, a 3D fluorescence image, a 2D hyperspectral image, or a 3D hyperspectral image.

[0163] Appendix 43. The method of Appendix 1, wherein the first data set and the second data set correspond to a first depth of the sample, and the method further includes adjusting a focus of the microscope to image the sample at a plurality of depths, and repeating the obtaining and combining steps to generate a computationally reconstructed image, the computationally reconstructed image comprising a plurality of computationally reconstructed images at different depths corresponding to the plurality of depths.

[0164] Clause 44. The method of clause 1, further comprising determining a first wavelength.

[0165] Clause 45. The method of clause 44, wherein the first wavelength is user-defined.

[0166] Clause 46. The method of clause 44, wherein determining the first wavelength further includes obtaining, using an image sensor, an initial image dataset of the sample illuminated with a plurality of wavelengths; determining that a first image of the initial image dataset contains more information than a second image of the initial image dataset; selecting as the first wavelength a first wavelength of the plurality of wavelengths corresponding to the first image; and selecting as the second wavelength a second wavelength of the plurality of wavelengths corresponding to the second image.

[0167] Clause 47. The method of Clause 46, wherein the information comprises spatial frequency information.

[0168] Clause 48. The method of clause 46, further comprising determining a first set of lighting conditions based on information identified from the initial image dataset.

[0169] Clause 49. The method of clause 48, wherein determining the first set of lighting conditions includes determining one or more of a number of light sources, a location of a light source, a combination of locations of multiple light sources, an angle of illumination, a combination of angles of illumination, a number of lights, a position of a diffuser, a light pattern, a filter, a mask, or a focus of the sample.

[0170] Clause 50. The method of clause 46, further comprising determining a computational process for reconstructing a first image based on the initial image dataset.

[0171] Clause 51. The method of Clause 1, wherein the computationally reconstructed image comprises a three-dimensional image.

[0172] Appendix 52. The method of Appendix 1, wherein the second set of lighting conditions is different from the first set of lighting conditions.

[0173] Addendum 53. The method of Addendum 1, wherein the second image dataset is smaller than the first image dataset.

[0174] Appendix 54. The method of Appendix 1, wherein the first image dataset is obtained after the second image dataset.

[0175] Clause 55. A microscope for image reconstruction, comprising: an illumination source configured to illuminate a sample with multiple wavelengths at multiple angles; an image sensor; an objective lens for imaging the sample illuminated with the illumination assembly onto the image sensor; and a processor operatively coupled to the illumination assembly and the image sensor, the processor configured with instructions for performing a method described in any one of the preceding clauses.

[0176] Clause 56. The microscope of clause 55, wherein the plurality of wavelengths comprises one or more of violet wavelengths in a range of about 380 to about 450 nanometers (nm), blue wavelengths in a range of about 450 to about 485 nm, cyan wavelengths in a range of about 485 to 500 nm, green wavelengths in a range of about 500 to 565 nm, yellow wavelengths in a range of about 565 to about 590 nm, orange wavelengths in a range of about 590 to 625 nm, red wavelengths in a range of about 625 to about 740 nm, infrared wavelengths greater than 700 nm, or near-infrared wavelengths in a range of about 700 nm to about 1100 nm.

[0177] Addendum 57. The microscope of Addendum 55, wherein the illumination assembly is configured to illuminate the sample using multiple light sources at multiple locations corresponding to different illumination angles of the sample.

[0178] Clause 58. The microscope of clause 55, further comprising a focus actuator coupled to the processor for adjusting a depth of the sample used to form an image of the sample on the image sensor.

[0179] Addendum 59. The microscope of Addendum 58, wherein the focus actuator is configured to move to a first configuration for imaging the sample at a first depth and to move to a second configuration for imaging the sample at a second depth.

[0180] The embodiments of the present disclosure are shown and described herein and are provided by way of example only. Those skilled in the art will recognize numerous adaptations, modifications, variations, and substitutions without departing from the scope of the present disclosure. Several alternatives and combinations of the embodiments disclosed herein may be utilized without departing from the scope of the present disclosure and invention disclosed herein. Accordingly, the scope of the presently disclosed invention is to be defined solely by the scope of the appended claims and their equivalents.

Claims

[Claim 1] The invention described in this specification.

Images