Digital imaging device and method for generating digital color images

Abstract

A microscope (100) includes a digital imaging device (106) having a processor (116), the processor (116) being configured to obtain a plurality of digital grayscale images (I1-I4) of an object (104) with a plurality of different spectral sensitivities (S1-S4), each digital grayscale image (I1-I4) including grayscale information based on a different one of the different spectral sensitivities (S1-S4). The processor (116) is further configured to obtain a plurality of weight factors (Mc) for each digital grayscale image (I1-I4), and to assign the weight factors to a plurality of color channels (R, G, B) defining a predetermined color space. The processor (116) is further configured to synthesize a digital color image of the object (104) from the digital grayscale images (I1-I4) in the color space by distributing the grayscale information of each grayscale image (I1-I4) on the color channels (R, G, B) according to the weight factors (Mc).

Description

Technical Field

[0001] The invention relates to a digital imaging device, a microscope comprising a digital imaging device and a method for generating a digital color image of an object. Background Art

[0002] In the field of microscopy, color cameras can be used to acquire images. Typically, such color cameras include a color filter array arranged on a grid of photosensors. In particular, a so-called Bayer color filter mosaic can be used as a color filter array for color imaging. A Bayer color filter mosaic includes green (G), red (R) and blue (B) color filters in predetermined proportions (typically 50% G, 25% R and 25% B).

[0003] The use of color cameras in microscopy allows the detection of the true color of an object, which is beneficial for example in the evaluation of samples in pathology and histology, and allows the staining of a sample to be determined before close examination. However, the incorporation of a color camera into a widefield or confocal microscope is usually very expensive. Therefore, monochrome imaging remains the method of choice in microscopy. Although it is conceivable to provide both a monochrome camera and a color camera in a microscope, it is difficult to achieve an exact temporal and spatial fit of the image data created in the different imaging modalities.

[0004] The article "Color image acquisition using monochrome camera and standard fluorescence filter cubes" (GF Weber, AS Menko, Biotechniques, Vol. 38, No. 1) discloses a method in which monochrome images are combined with the aid of graphics software. However, this method is not suitable for automatically creating color images based on monochrome imaging. Summary of the invention

[0005] The object is to provide a digital imaging device and method which allow generating a digital color image of an object based on a digital grayscale image in a reliable manner.

[0006] The microscope includes a digital imaging device, the digital imaging device including a processor, the processor being configured to obtain a plurality of digital grayscale images of an object at a plurality of different spectral sensitivities, wherein each digital grayscale image includes grayscale information based on a different one of the different spectral sensitivities. The processor is further configured to obtain a plurality of weight factors for each digital grayscale image, and to assign the weight factors to a plurality of color channels defining a predetermined color space. The processor is further configured to synthesize a digital color image of the object in the color space from the digital grayscale images by distributing the grayscale information of each grayscale image across the color channels according to the weight factors.

[0007] The microscope can be used as a virtual color camera by transforming a plurality of spectral input channels represented by different spectral sensitivities of digital grayscale images into a plurality of spectral output channels given by color channels defining a color space of a color camera to be simulated by the digital imaging device. Thus, each grayscale image belonging to one of the input channels represented by the different spectral sensitivities is weighted relative to the model of the color camera to be simulated by the digital imaging device.

[0008] The color space into which the digital grayscale image is transformed may be one of the typical color spaces used in the field, such as RGB (red, green, blue), CMYK (cyan, magenta, yellow, black), and the like.

[0009] Preferably, the number of spectral sensitivities of the grayscale image is different from and in particular greater than the number of color channels of the color space. The greater the number of input channels represented by the different spectral sensitivities of the grayscale image, the higher the image information available for creating a digital color image. Thus, the quality of the digital color image increases with the number of spectral sensitivities of the grayscale image. Color imaging as disclosed herein can be achieved regardless of whether the input spectra represented by the spectral sensitivities of the grayscale image overlap. Furthermore, input spectra outside the visible range, such as infrared (IR) light, can be processed.

[0010] The weighting factors can be represented by a matrix whose number of rows is equal to the number of spectral input channels, i.e. the number of digital grayscale images, and whose number of columns is equal to the number of output channels, i.e. the number of color channels of the camera model to be simulated. Therefore, the determination of digital color images based on different digital grayscale images comes down to a simple matrix multiplication that facilitates real-time color imaging.

[0011] The weighting factors can be predetermined before acquiring the digital grayscale images, based on which the grayscale information of each digital grayscale image is distributed on different color channels of the camera model. Therefore, the computational workload required for synthesizing digital color images is low, and real-time color imaging can be easily achieved.

[0012] Preferably, the grayscale information of each digital grayscale image is corrected for the sensor quantum yield. The quantum yield of the sensor is a device parameter known in advance. Therefore, the quantum yield of the sensor may have been taken into account when determining the weighting factor. Thus, the correction for the sensor quantum yield does not affect the real-time color imaging.

[0013] The weighting factor may be corrected according to the Bayer color filter. For example, in the case of applying the RGB camera model, the Bayer color filter correction gives a greater weight to the G component having a value of 0.5 than to the R and B components having values ​​of 0.25 respectively.

[0014] The processor may be configured to determine a maximum value from all image values ​​of the digital color image and normalize the image values ​​based on the maximum value. For example, in the case where the digital color image should be processed in the form of an 8-bit image including values ​​from 0 to 256, a scaling factor of 256 / Max is calculated, wherein Max indicates the aforementioned maximum value, and all image values ​​of the digital color image are normalized using this scaling factor.

[0015] The processor may further be configured to perform white balancing on the digital color image. Thus, the quality of the digital color image may be improved.

[0016] According to a preferred embodiment, the microscope includes a plurality of image sensors configured to acquire the plurality of digital grayscale images with a plurality of different spectral sensitivities. Thus, grayscale images can be acquired sequentially or simultaneously by means of a plurality of image sensors having different sensitivities in the spectral domain. However, the digital imaging device is not limited thereto. For example, the digital imaging device can also be used to post-process grayscale images created by a single image sensor and a plurality of color filters, which are sequentially introduced into the optical path leading to the image sensor to create a sequence of grayscale images of different spectra. Since the spectral characteristics of the color filters are known in advance, the corresponding weight factors can be determined, and a post-processing process can be performed on the grayscale image sequence to synthesize a digital color image.

[0017] Each image sensor may be formed of a wide field sensor, but is not limited thereto. For example, the digital imaging device may also be used in a confocal microscope using a point sensor to sequentially acquire a digital grayscale image pixel by pixel.

[0018] Preferably, the microscope comprises a display device configured to display the digital color image in real time.

[0019] Preferably, the microscope may be a confocal microscope or a wide-field microscope as described above, but is not limited thereto.

[0020] According to another aspect, a method for generating a digital color image of an object is provided, wherein the method comprises the following steps: obtaining a plurality of digital grayscale images of the object with a plurality of different spectral sensitivities using a microscope, each digital grayscale image comprising grayscale information based on a different one of the different spectral sensitivities; obtaining a plurality of weight factors for each digital grayscale image, assigning the weight factors to a plurality of color channels defining a predetermined color space; and synthesizing a digital color image of the object from the digital grayscale images in the color space by distributing the grayscale information of each grayscale image on the color channel according to the weight factors.

[0021] For example, the weighting factors may be obtained by reading the weighting factors from a memory. Thus, the weighting factors do not need to be determined during the imaging process. Instead, if the spectral sensitivity of the digital grayscale image and the color channels of the camera model utilized are known in advance, the weighting factors may be determined prior to the imaging process and stored in the memory. The memory may then be accessed to retrieve the weighting factors when acquiring the image.

[0022] According to another aspect, a computer program having a program code for executing the provided method is provided. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] In the following, specific embodiments will be described with reference to the accompanying drawings, in which:

[0024] Figure 1 is a block diagram showing an embodiment of a digital imaging device;

[0025] Figure 2 is a schematic diagram showing a process performed by a digital imaging device for generating a digital color image; and

[0026] Figure 3 A schematic diagram is shown of a system configured to perform the methods described herein. DETAILED DESCRIPTION

[0027] Figure 1 A schematic diagram showing a fluorescence microscope including a digital imaging device according to one embodiment; and

[0028] Figure 2 To show that Figure 1 A schematic diagram of a process for synthesizing a digital color image performed by a digital imaging device is shown.

[0029] Figure 1 To illustrate a block diagram of a fluorescence microscope 100, the fluorescence microscope 100 may include an object slide 102 on which an object 104 is placed. The microscope 100 includes a digital imaging device 106 configured to generate a plurality of digital grayscale images of the object 104 at a plurality of different spectral sensitivities. To this end, the digital imaging device 106 may include a corresponding number of image sensors 108, 110, 112, and 114. Figure 1 In FIG. 1 , each image sensor 108 to 114 obtains a digital grayscale image labeled I1, I2, I3, and I4. The image sensors 108 to 114 may be formed by CMOS sensors, but are not limited thereto. In order to obtain grayscale images I1 to I4 in different spectrums, a suitable filter ( Figure 1 ), each of which transmits only the fluorescence emitted from the object 104 in a specific wavelength region toward the image sensor.

[0030] The digital imaging device 106 also includes a processor 116, which can be configured to control the overall operation of the fluorescence microscope 100. Specifically, the processor 116 is used to control the following reference Figure 2 The color imaging process is explained in more detail. To this end, a processor 116 is coupled to the image sensors 108 to 114 for obtaining digital grayscale images I1 to I4, which are to be processed for color imaging.

[0031] The imaging device 106 may also include a display device 118 and a memory 120 that are also coupled to the processor 116 .

[0032] In the following, it may be assumed that the fluorescence microscope 100 is formed by a wide-field microscope so that each of the image sensors 108 to 114 obtains a wide-field image of the object 104. However, the fluorescence microscope 100 is not limited to the wide-field configuration. For example, a confocal microscope configuration may be applied in which each of the image sensors 108 to 114 is formed by a point sensor and each of the digital grayscale images I1 to I4 includes a plurality of pixels sequentially obtained by the corresponding image sensors 108 to 114.

[0033] Figure 2 1 is a schematic diagram showing a process performed by the digital imaging device 106 for synthesizing a digital color image of the object 104 based on the digital grayscale images I1 to I4 obtained by the image sensors 108 to 114. In the following example, it is assumed that the digital color image is an RGB image corresponding to the superposition of three color images of R, G and B. Needless to say, Figure 2 The process shown in can also be applied to another color space, such as the CMYK color space.

[0034] First, as shown in block B1 and FIG. D1, processor 116 obtains normalized data representing different spectral sensitivities of image sensors 108 to 114. Specifically, image sensor 108 may have a spectral sensitivity as indicated by curve S1 in FIG. D1. Similarly, curve S2 indicates the spectral sensitivity of image sensor 110, curve S3 indicates the spectral sensitivity of image sensor 112, and curve S4 indicates the spectral sensitivity of image sensor 114. As can be seen in FIG. D1, the spectral wavelength regions corresponding to spectral sensitivities S1 to S4 are substantially separated from each other, i.e., do not overlap. According to FIG. Figure 2 In FIG. D1 , the spectral sensitivities S1 , S2 , S3 , S4 may correspond substantially to blue, green, yellow and red, respectively. As explained below, the spectral sensitivities S1 to S4 define four input channels, which will be transformed into a color space for emulating the RGB space of an RGB color camera.

[0035] Then, if Figure 2As shown in block B2 and FIG. D2 in FIG. 1 , a normalized quantum yield Y of each image sensor 108 to 114 is obtained. The spectral sensitivities S1 to S4 of the image sensors 108 to 114 are multiplied by the quantum yield Y, so that quantum yield-corrected spectral sensitivities S1a, S2a, S3a, and S4a are obtained, as shown in block B3 and FIG. D3 .

[0036] In the next step, as shown in box B4 and Figure D4, the normalized model sensitivities SB, SG, and SR of the RGB camera model are considered. Using the RGB color space as the target color space, the model sensitivities SB, SG, and SR are generally determined based on the Bayer color filter. Specifically, the three spectral model sensitivities SB, SG, and SR define the target model to which the spectral sensitivities S1, S2, S3, and S4 of the image sensors 108 to 112 are to be mapped. In Figure D4, the model sensitivity SB corresponds to the color B, the model sensitivity SG corresponds to the color G, and the model sensitivity SR corresponds to the color R. As a characteristic of the Bayer color filter, the spectral model characteristic SG corresponding to G is overestimated relative to the model sensitivities SB and SR corresponding to B and R, respectively.

[0037] like Figure 2 As shown in the box B5 and the matrix M in FIG. D1, the mapping of the spectral sensitivities S1, S2, S3, S4 of FIG. D1 to the RGB target model defined by the model sensitivities SB, SG, SR of FIG. D4 is achieved by weighting factors. Specifically, the matrix M includes three columns corresponding to the three output color channels R, G, B of the target system, and the matrix M includes four rows corresponding to the four spectral sensitivities S1, S2, S3, S4 of the image sensors 108 to 114. Therefore, each column R, G and B of the matrix M includes four values ​​corresponding to the four spectral sensitivities S1, S2, S3, S4, respectively. Each of these values ​​specifies a weighting factor that determines the ratio of mapping each spectral sensitivity S1, S2, S3, S4 to the corresponding color channel R, G and B. For example, the first row of the matrix M indicates that the first spectral sensitivity S1 is mapped to the color channel R with a weighting factor of 0.092, to the color channel G with a weighting factor of 0.28, and to the color channel B with a weighting factor of 0.628. Likewise, according to the second row of the matrix M, the spectral sensitivity S2 is mapped to the color channel R with a weight factor of 0.08, to the color channel G with a weight factor of 0.525, and to the color channel B with a weight factor of 0.395. According to the third row of the matrix, the spectral sensitivity S3 is mapped to the color channel R with a weight factor of 0.667, to the color channel G with a weight factor of 0.062, and to the color channel B with a weight factor of 0.271. According to the fourth row of the matrix M, the spectral sensitivity S4 is mapped to the color channel R with a weight factor of 0.605, to the color channel G with a weight factor of 0.053, and to the color channel B with a weight factor of 0.342.

[0038] In this regard, it should be noted that Figure 2 The symbol "*" between the middle boxes B3 and B4 indicates a mapping function, which is applied to transform the four input channels represented by the spectral sensitivities S1, S2, S3 and S4 into three output channels represented by the color channels R, G and B. Since the input channels S1, S2, S3, S4 and the output channels R, G, B are known in advance, a suitable mapping function can be determined for achieving the mapping from the input channels to the output channels.

[0039] Subsequently, as shown in block B6, the matrix M obtained in block B5 is multiplied by a vector V including correction values ​​0.25, 0.5, and 0.25 determined according to the Bayer color filter. Thus, as shown in block B7, a corrected model matrix Mc including corrected weight factors is obtained, which can be stored in the memory 120.

[0040] Based on the model matrix Mc including the corrected weight factors, color imaging can be performed in real time. To this end, as shown in block B8, grayscale images I1 to I4 are acquired by means of image sensors 108 to 114. Subsequently, as shown in block B9, the model matrix Mc is retrieved from the memory 120, and the processor 116 distributes the grayscale information included in each grayscale image I1 to I4 on the color channels R, G and B according to the weight factors of the model matrix Mc.

[0041] Specifically, according to the first row of Mc, the grayscale information of the first grayscale image I1 assigned to the spectral sensitivity S1 is mapped to the color channel R with a weight factor of 0.072, mapped to the color channel G with a weight factor of 0.438, and mapped to the color channel B with a weight factor of 0.491. Similarly, according to the second row of Mc, the grayscale information of the second grayscale image I2 assigned to the spectral sensitivity S2 is mapped to the color channel R with a weight factor of 0.053, mapped to the color channel G with a weight factor of 0.689, and mapped to the color channel B with a weight factor of 0.259. According to the third row of Mc, the grayscale information of the third grayscale image I3 assigned to the spectral sensitivity S3 is mapped to the color channel R with a weight factor of 0.628, mapped to the color channel G with a weight factor of 0.116, and mapped to the color channel B with a weight factor of 0.256. According to the fourth row of Mc, the grayscale information of the fourth grayscale image I4 assigned to the spectral sensitivity S4 is mapped to the color channel R with a weight factor of 0.575, to the color channel G with a weight factor of 0.100, and to the color channel R with a weight factor of 0.325. Thus, image values ​​in the color channels R, G, and B are obtained based on the grayscale information included in the grayscale images I1 to I4.

[0042] Then, as shown in block B10, the processor 116 may determine the maximum value from all the image values ​​obtained in block B9. In addition, as shown in block B11, the processor 116 may determine a scale factor and normalize the image values ​​using the scale factor. Subsequently, as shown in block B12, the processor 116 may perform a white balance process. Finally, the resulting digital RGB color image is displayed on the display device 118.

[0043] It should be noted that the model matrix Mc including the weighting factors Mc can be determined based on acquiring the grayscale images I1 to I4 prior to real-time color imaging. In particular, since the matrix Mc can be calculated independently of the grayscale images I1 to I4, the matrix Mc will only be determined once and can be stored for further imaging.

[0044] Furthermore, it should be noted that the process explained above includes optional steps that may be omitted. For example, depending on the specific image sensor used, it may not be necessary to normalize the spectral sensitivities S1 to S4 based on the quantum yield Y as shown in block B2. Likewise, in the case of applying other model color spaces, the Bayer matrix correction may be omitted.

[0045] As used herein, the term "and / or" includes any and all combinations of one or more of the associated listed items and may be abbreviated as " / ".

[0046] Although some aspects have been described in the context of an apparatus, it is clear that these aspects also represent a description of a corresponding method, where a block or device corresponds to a method step or a feature of a method step. Similarly, aspects described in the context of a method step also represent a description of a corresponding block or item or feature of a corresponding apparatus.

[0047] Some embodiments relate to a microscope comprising a microscope incorporating Figures 1 to 2 Alternatively, the microscope may be combined with Figures 1 to 2 Part of or connected to one or more of the described systems. Figure 3A schematic diagram of a system 300 configured to perform the methods described herein is shown. The system 300 includes a microscope 310 and a computer system 320. The microscope 310 is configured to capture images and is connected to the computer system 320. The computer system 320 is configured to perform at least a portion of the methods described herein. The computer system 320 can be configured to execute a machine learning algorithm. The computer system 320 and the microscope 310 can be separate entities, but can also be integrated together in a common housing. The computer system 320 can be part of a central processing system of the microscope 310 and / or the computer system 320 can be part of a subcomponent of the microscope 310, such as a sensor, an actuator, a camera, or an illumination unit of the microscope 310, etc.

[0048] The computer system 320 may be a local computer device (e.g., a personal computer, laptop computer, tablet computer, or mobile phone) having one or more processors and one or more storage devices, or may be a distributed computer system (e.g., a cloud computing system having one or more processors and one or more storage devices distributed in various locations such as a local client and / or one or more remote server clusters and / or data centers). The computer system 320 may include any circuit or combination of circuits. In one embodiment, the computer system 320 may include one or more processors that may be of any type. As used herein, a processor may refer to any type of computing circuit, such as, but not limited to, a microprocessor, microcontroller, complex instruction set computing (CISC) microprocessor, reduced instruction set computing (RISC) microprocessor, very long instruction word (VLIW) microprocessor, graphics processor, digital signal processor (DSP), multi-core processor, field programmable gate array (FPGA), or any other type of processor or processing circuit. Other types of circuits that may be included in the computer system 320 may be custom circuits, application specific integrated circuits (ASICs), etc., such as one or more circuits (e.g., communication circuits) that are used in wireless devices such as mobile phones, tablet computers, laptops, two-way radios, and similar electronic systems. The computer system 320 may include one or more storage devices, which may include one or more storage elements suitable for a particular application, such as main memory in the form of random access memory (RAM), one or more hard disk drives, and / or one or more drives for handling removable media, such as compact disks (CDs), flash memory cards, digital video disks (DVDs), etc. The computer system 320 may also include a display device, one or more speakers, and a keyboard and / or controller, which may include a mouse, trackball, touch screen, voice recognition device, or any other device that allows a system user to input information into and receive information from the computer system 320.

[0049] Some or all of the method steps may be performed by (or using) a hardware device (e.g., a processor, a microprocessor, a programmable computer or an electronic circuit). In some embodiments, such a device may perform one or more of some of the most important method steps.

[0050] Depending on certain implementation requirements, embodiments of the present invention may be implemented in hardware or software. This embodiment may be performed using a non-transitory storage medium, such as a digital storage medium, for example a floppy disk, DVD, Blu-ray, CD, ROM, PROM and EPROM, EEPROM or FLASH memory, on which electronically readable control signals are stored that cooperate (or are capable of cooperating) with a programmable computer system to perform the corresponding method. Thus, the digital storage medium may be computer readable.

[0051] Some embodiments according to the invention comprise a data carrier having electronically readable control signals, which are capable of cooperating with a programmable computer system, such that one of the methods described herein is performed.

[0052] Generally, the embodiments of the present invention can be implemented as a computer program product with a program code, which can be used to perform one of the methods when the computer program product runs on a computer.The program code can, for example, be stored on a machine-readable carrier.

[0053] Other embodiments comprise the computer program for performing one of the methods described herein, stored on a machine readable carrier.

[0054] In other words, therefore, an embodiment of the inventive method is a computer program having a program code for performing one of the methods described herein, when the computer program runs on a computer.

[0055] Therefore, another embodiment of the present invention is a storage medium (or a data carrier or a computer-readable medium) comprising a computer program stored thereon for performing one of the methods described herein when executed by a processor. The data carrier, the digital storage medium or the recorded medium is typically tangible and / or non-transitory. Another embodiment of the present invention is a device as described herein, comprising a processor and a storage medium.

[0056] A further embodiment of the invention is, therefore, a data stream or a sequence of signals representing the computer program for performing one of the methods described herein.The data stream or the sequence of signals may, for example, be configured to be transmitted via a data communication connection, for example via the Internet.

[0057] A further embodiment comprises a processing means, for example a computer or a programmable logic device, configured to or adapted to perform one of the methods described herein.

[0058] A further embodiment comprises a computer having installed on it the computer program for performing one of the methods described herein.

[0059] Another embodiment according to the invention comprises an apparatus or system configured to transmit (e.g. electronically or optically) a computer program for performing one of the methods described herein to a receiver. The receiver may be, for example, a computer, a mobile device, a storage device, etc. The apparatus or system may, for example, comprise a file server for transmitting the computer program to the receiver.

[0060] In some embodiments, a programmable logic device (e.g., a field programmable gate array) can be used to perform some or all of the functions of the method described herein. In some embodiments, a field programmable gate array can collaborate with a microprocessor to perform one of the methods described herein. Typically, the method is preferably performed by any hardware device.

[0061] Reference Symbols List

[0062] 100 Fluorescence Microscope

[0063] 102 Imaging Unit

[0064] 104 Objects

[0065] 106 Digital imaging device

[0066] 108-114 Image Sensor

[0067] 116 Processors

[0068] 118 Display device

[0069] 120 Memory

[0070] 300 Systems

[0071] 310 Microscope

[0072] 320 Computer Systems

[0073] S1-S4 Spectral Sensitivity

[0074] S1a-S4a Corrected Spectral Sensitivity

[0075] Y sensor quantum yield

[0076] SB, SG, SR model sensitivity

[0077] B1-B12 Box

[0078] D1-D4 Figure

[0079] M, Mc matrix

[0080] V Vector

Claims

1. A microscope (100), the microscope (100) comprising a digital imaging device (106), the digital imaging device (106) comprising a processor (116), the processor (116) being configured to: obtaining a plurality of digital grayscale images (I1-I4) of the object (104) at a plurality of different spectral sensitivities (S1-S4), each digital grayscale image (I1-I4) comprising grayscale information based on a different one of the different spectral sensitivities (S1-S4), obtaining a plurality of weighting factors (Mc) for each digital grayscale image (I1-I4), assigning the weighting factors to a plurality of color channels (R, G, B) defining a predetermined color space, and synthesizing a digital color image of an object (104) in said color space from said digital grayscale images (I1-I4) by distributing the grayscale information of each grayscale image (I1-I4) over the color channels (R, G, B) according to a weighting factor (Mc); in, The digital imaging device (106) further comprises a plurality of image sensors (108-114), wherein the plurality of image sensors (108-114) are configured to obtain the plurality of digital grayscale images (I1-I4) with the plurality of different spectral sensitivities (S1-S4), wherein the plurality of image sensors (108-114) correspond to the plurality of different spectral sensitivities (S1-S4).

2. The microscope (100) according to claim 1, wherein: The number of spectral sensitivities (S1-S4) of the grayscale images (I1-I4) is different from the number of color channels (R, G, B) of the color space.

3. Microscope (100) according to claim 1 or 2, wherein the weighting factors are represented by a matrix (Mc) whose number of rows is equal to the number of digital grayscale images (I1-I4) and whose number of columns is equal to the number of color channels (R, G, B) of the color space.

4. The microscope (100) according to claim 1 or 2, wherein: The grayscale information of each digital grayscale image (I1-I4) is corrected for the sensor quantum yield (Y).

5. The microscope (100) according to claim 1 or 2, wherein: The weighting factor (Mc) is corrected for the Bayer color filter.

6. The microscope (100) according to claim 1 or 2, wherein: The processor (116) is configured to determine a maximum value from all image values ​​of the digital color image and to normalize the image values ​​based on the maximum value.

7. The microscope (100) according to claim 1 or 2, wherein: The processor (116) is configured to perform white balancing on the digital color image.

8. The microscope (100) according to claim 1, wherein: Each image sensor (108-114) is formed by a wide field sensor.

9. The microscope (100) according to claim 1 or 2, comprising a display device (118), the display device (118) being configured to display the digital color image in real time.

10. The microscope (100) according to claim 1 or 2, wherein: The color space is an RGB color space.

11. A method for generating a digital color image of an object (104), comprising the steps of: Using a microscope, a plurality of digital grayscale images (I1-I4) of an object (104) are obtained at a plurality of different spectral sensitivities (S1-S4), each digital grayscale image (I1-I4) comprising grayscale information based on a different one of the different spectral sensitivities (S1-S4), obtaining a plurality of weighting factors (Mc) for each digital grayscale image, assigning the weighting factors to a plurality of color channels (R, G, B) defining a predetermined color space, and synthesizing a digital color image of the object from the digital grayscale images (I1-I4) in said color space by distributing the grayscale information of each grayscale image (I1-I4) over the color channels (R, G, B) according to a weighting factor (Mc); The microscope comprises a plurality of image sensors (108-114), the plurality of image sensors (108-114) being configured to obtain the plurality of digital grayscale images (I1-I4) with the plurality of different spectral sensitivities (S1-S4), wherein the plurality of image sensors (108-114) correspond to the plurality of different spectral sensitivities (S1-S4).

12. The method according to claim 11, wherein: The weighting factor (Mc) is obtained by reading the same weighting factor (Mc) from the memory (120).

13. A non-transitory storage medium for storing a computer program for performing the method according to claim 11 or 12 when the computer program is run on a processor (116).

Images