Saturation Avoidance in Digital Imaging

Abstract

A method of image capture helps avoid saturation in digital imaging. In one implementation, the method includes capturing a first digital image of a target using an electronic array light sensor, and identifying one or more saturated pixels in the first digital image. The method further includes identifying a region of interest in the first digital image, the region of interest containing one or more identified saturated pixels. The method also includes capturing a second digital image of the target using the electronic array light sensor. The second digital image contains only the region of interest, and the second digital image is captured with an exposure time that is shorter than the exposure time of the first digital image. The first digital image and the second digital image can be combined into a high dynamic range image. The system for digital imaging can be based on a complementary metal oxide semiconductor (CMOS) or a charge coupled device (CCD) sensor.

Description

[0001] CROSS-REFERENCE TO RELATED APPLICATIONS

[0002] This application claims the benefit of priority to U.S. Provisional Patent Application No. 62 / 745,610, filed on October 15, 2018, entitled “Saturation Avoidance in Digital Imaging,” the contents of which are hereby incorporated by reference in their entirety for all purposes. Background Art

[0003] Electrophoresis is a technique used in molecular biology and other disciplines to detect the presence of proteins or other analytes in a mixture. Typically, after some preparatory steps, the mixture is placed in the "wells" of a gel, such as an agarose or polyacrylamide gel. For protein determination, the gel is subjected to an electric field, which causes the proteins to migrate through the gel. The rate at which a particular protein in a mixture migrates through the gel depends on its molecular weight. Proteins with smaller molecular weights tend to migrate faster than proteins with larger molecular weights. Over time, different proteins are separated because they travel different distances through the gel.

[0004] Proteins can be labeled with antibodies so that the protein of interest emits light through chemiluminescence. In some applications, such as in the well-known Western blotting technique, proteins are transferred to a membrane such as polyvinylidene fluoride (PVDF) or nitrocellulose to form a blot. Historically, (after several incubation steps) the blot is placed in contact with a photosensitive film of approximately the same size as the blot. Chemiluminescence exposes various parts of the film so that the pattern of protein separation is permanently recorded on the film. Recently, electronic imaging is replacing photosensitive film to achieve this purpose.

[0005] Contact imagers have been proposed for reading images of blots. In a contact imager, a membrane is placed in contact or effectively in contact with a large semiconductor light sensor, such as a CMOS (complementary metal oxide semiconductor) or CCD (charge coupled device) sensor. The light emitted by the chemiluminescence reaches the sensor directly in much the same way as in film-based contact recording. In this method, there is no shutter or other means to block light from the sample from reaching the sensor, so the sensor is continuously exposed to the chemiluminescent light from the sample. In addition, parts of the blot may emit much more light than other parts, requiring the imaging system to have an extremely high dynamic range. Summary of the Invention

[0006] According to a first aspect, a method of image capture includes capturing a first digital image of a target using an electronic array light sensor and identifying one or more saturated pixels in the first digital image. The method further includes identifying a region of interest in the first digital image. The region of interest includes at least some of the one or more identified saturated pixels. The method further includes capturing a second digital image of the target using the electronic array light sensor, the second digital image including only the region of interest. The second digital image is captured with an exposure time shorter than the exposure time of the first digital image. In some embodiments, the first digital image includes the entire electronic array light sensor, and the first digital image is read from the electronic array light sensor as quickly as possible. In some embodiments, the region of interest includes all saturated pixels in the first digital image. In some embodiments, the region of interest includes a discrete patch of saturated pixels. In some embodiments, the region of interest includes only one of at least two discrete patches of saturated pixels. In some embodiments, the method further includes assembling a high dynamic range digital image of the target using at least the first digital image and the second digital image. In some embodiments, the method further includes: capturing a long-exposure digital image of the target using an electronic array light sensor, the long-exposure digital image being captured with an exposure time longer than the exposure time of the first digital image; and assembling a high dynamic range digital image using at least the first digital image, the second digital image, and the long-exposure digital image. In some embodiments, the region of interest is a first region of interest, and the method further includes: identifying one or more saturated pixels in the second digital image; identifying a second region of interest that includes at least some of the saturated pixels in the second digital image, the second region of interest being smaller than the first region of interest; and capturing a third digital image of the target using the electronic array light sensor, the third digital image including only the second region of interest, and the third digital image being captured with an exposure time shorter than the exposure time of the second digital image. In some embodiments, the method further includes assembling a high dynamic range digital image of the target using at least the first digital image, the second digital image, and the third digital image. In some embodiments, the method further includes: capturing a long exposure digital image of the target using the electronic array light sensor, the long exposure digital image being captured with an exposure time longer than an exposure time of the first digital image; and assembling a high dynamic range digital image of the target using at least the first digital image and the long exposure digital image, wherein the second region of interest in the high dynamic range digital image includes data derived from the third digital image.In some embodiments, the region of interest is a first region of interest, and the method further includes: identifying one or more saturated pixels in the second digital image; subdividing the first region of interest into one or more progressively smaller regions of interest; and capturing one or more additional digital images of the one or more progressively smaller regions of interest using progressively smaller exposure times until a digital image free of saturated pixels is obtained. In some embodiments, the electronic array light sensor is a complementary metal oxide semiconductor (CMOS) sensor, and capturing the second digital image of the object includes reading fewer than all pixels in the electronic array light sensor. In some embodiments, the electronic array light sensor is a complementary metal oxide semiconductor (CMOS) sensor, and capturing at least one of the first digital image and the second digital image includes using a rolling shutter. In some embodiments, the electronic array light sensor is a charge coupled device (CCD) sensor, and capturing the second digital image of the object includes transferring some charge from the CCD sensor and discarding it without converting it to a digital value. In some embodiments, the electronic array light sensor is a charge coupled device (CCD) sensor, and capturing the second digital image of the object includes binning the charge in the CCD sensor. In some embodiments, the method further includes limiting the size of the region of interest relative to the electronic array light sensor. In some embodiments, the second digital image is captured at a lower resolution than the first digital image.

[0007] According to another aspect, an imaging device includes an electronic array light sensor having a plurality of pixels and a controller programmed to control the operation of the electronic array light sensor and to receive signals from the electronic array light sensor indicating light intensities respectively falling on the pixels of the electronic array light sensor. The controller is programmed to: capture a first digital image of a target using the electronic array light sensor, identify one or more saturated pixels in the first digital image, and identify a region of interest in the first digital image, the region of interest containing the one or more identified saturated pixels. The controller is further programmed to capture a second digital image of the target using the electronic array light sensor, the second digital image containing only the region of interest and the second digital image being captured with an exposure time shorter than that of the first digital image. In some embodiments, the electronic array light sensor is a complementary metal oxide semiconductor (CMOS) sensor or a charge coupled device (CCD) sensor. In some embodiments, the controller is further programmed to construct a high dynamic range digital image of the target using at least the first digital image and the second digital image. In some embodiments, the controller is further programmed to: identify one or more saturated pixels in the second digital image; identify a second region of interest that includes at least some of the saturated pixels in the second digital image, the second region of interest being smaller than the first region of interest; and capture a third digital image of the target using the electronic array light sensor, the third digital image including only the second region of interest, and the third digital image being captured with an exposure time that is shorter than the exposure time of the second digital image. In some embodiments, the electronic array light sensor includes a plurality of taps. BRIEF DESCRIPTION OF THE DRAWINGS

[0008] Figure 1 The figure shows a typical blot after separation of proteins according to an embodiment of the present invention.

[0009] Figure 2 An imaging device according to an embodiment of the present invention is shown in a closed position.

[0010] Figure 3 Shown in open position Figure 2 imaging equipment.

[0011] Figure 4 The figure shows Figure 1 The blot is similar to the blot form, and is placed such as Figure 2 An imaging device such as a device contacts an area of ​​the target on an image sensor.

[0012] Figure 5 Figure shows a simplified block diagram of a CMOS image sensor according to an embodiment of the present invention.

[0013] Figure 6 The diagram shows that Figure 4 A digital image captured of the target.

[0014] Figure 7 Shown with additional annotations Figure 6 digital image.

[0015] Figure 8 The figure shows a flow chart of a method according to an embodiment of the present invention.

[0016] Figure 9 The figure shows the ratio Figure 6 Faster capture of digital images Figure 7 A digital image of a region of the digital image.

[0017] Figure 10 Depicted by Figure 6 and Figure 9 A high dynamic range digital image assembled from digital images.

[0018] Figure 11 Another digital image according to an embodiment of the present invention is shown.

[0019] Figure 12 Another digital image according to an embodiment of the present invention is shown.

[0020] Figure 13 Another digital image according to an embodiment of the present invention is shown.

[0021] Figure 14 The figure shows a flow chart of a method according to an embodiment of the present invention.

[0022] Figure 15 Another digital image according to an embodiment of the present invention is shown.

[0023] Figure 16 Another digital image according to an embodiment of the present invention is shown.

[0024] Figure 17 Another digital image according to an embodiment of the present invention is shown.

[0025] Figure 18 Shows the effect of shorter exposure time on Figure 17 The result of imaging certain areas of a digital image.

[0026] Figure 19 The diagram illustrates the initial steps of constructing a high dynamic range image according to an embodiment of the present invention.

[0027] Figure 20 FIG. 4 illustrates another step of constructing a high dynamic range image according to an embodiment of the present invention.

[0028] Figure 21FIG. 4 illustrates another step of constructing a high dynamic range image according to an embodiment of the present invention.

[0029] Figure 22 The figure illustrates a rolling shutter according to an embodiment of the present invention.

[0030] Figure 23 The figure shows a simplified block diagram of a CCD image sensor according to an embodiment of the present invention.

[0031] Figure 24 The diagram illustrates an imaging system according to an embodiment of the present invention. DETAILED DESCRIPTION

[0032] Figure 1 The figure shows a typical blot 100 after protein separation according to an embodiment of the present invention. One lane 101 of the blot is reserved for protein standards 102a-102f. Protein standards 102a-102f have been separated in direction 103, wherein the smaller molecular weight protein standard 102f is further away from the edge 104 than the larger molecular weight protein standard 102a.

[0033] A sample of the mixture to be assayed has been separated in lanes 105-110, resulting in protein bands 111, 112, and 113 in each of lanes 105-110. (The lane divisions shown in dashed lines are for illustration purposes only and do not appear on the blot.) Protein band 111 luminesces strongly and corresponds approximately to the molecular weight of protein standard 102b. Protein band 112 luminesces slightly less strongly and corresponds to the molecular weight of protein standard 102e. Protein band 113 luminesces very weakly, corresponding approximately to the molecular weight of protein standard 102c, indicating that very little protein may be present in band 113. Because the molecular weights of standards 102b, 102c, and 102e are known, their presence provides information about the molecular weights of the proteins in bands 111, 112, and 113, aiding in identifying the proteins in bands 111, 112, and 113.

[0034] like Figure 1 As indicated, the proteins in bands 111, 112, and 113 emit light via chemiluminescence. Protein standards 102a-102f may or may not be chemiluminescent.

[0035] Figure 2 An imaging device 200 according to an embodiment of the present invention is shown in a closed position. Figure 3 The imaging device 200 is shown in an open position.

[0036] refer to Figure 2 and Figure 3In both cases, the imaging device 200 includes a base portion 201 and a cover 202. The cover 202 is Figure 2 is shown in the closed position, while Figure 3 , and is shown in the open position. The contact area image sensor 301 is disposed in the base 201. The contact area image sensor 301 can be an example of an electronic array light sensor and can be of the type described in, for example, U.S. Patent Application Publication No. 2015 / 0172526, published on June 18, 2015, and entitled “Non-Destructive Read Operations with Dynamically Growing Images” by Swihart et al. (now U.S. Patent No. 9,736,388); U.S. Patent Application Publication No. 2016 / 0006910, published on January 7, 2016, and entitled “Contact Imager” by Uri et al. (now U.S. Patent No. 9,794,454); and Ran et al., published on January 28, 2016, and entitled “Digital Masking Pixels with Masked No. 2016 / 0028976 (now U.S. Patent No. 9,774,804), entitled “Digital Mask Pixels with Masked Pixels”; and U.S. Patent Application Publication No. 2017 / 0016829 (now U.S. Patent No. 9,933,565), published on January 19, 2017, to Swihart et al., entitled “Contact Imaging Devices for Fluorescence Applications”; the disclosures of all of these patent applications are incorporated herein by reference in their entirety for all purposes.

[0037] The contact area image sensor 301 can be, for example, a charge coupled device (CCD) sensor, a complementary metal oxide semiconductor (CMOS) sensor, an organic photodiode sensor, or another suitable type of sensor. Typically, such sensors utilize the property of certain semiconductor materials, namely that when the material is struck by light, free electrons proportional to the light intensity are generated. The sensor is divided into specific photosensitive areas, called "pixels." To capture an image, the pixels are reset and then exposed for an exposure time. At the end of the exposure time, the amount of charge accumulated in each pixel is measured and converted into a numerical value. The array of these numerical values ​​can be called a "digital image," where each value in the array represents the brightness of the light falling on the corresponding pixel. In a digital image, these values ​​can also be called pixels.

[0038] In a CCD sensor, the accumulated charge is transferred away from the sensor to a charge amplifier, whose output is digitized for each pixel. In a CMOS sensor, the accumulated charge can be read directly from each pixel without transferring the charge. In some sensors, different pixels are sensitive to different wavelengths of light, enabling color imaging.

[0039] In this context, a "contact" sensor is one that receives light directly from a location on the target that contacts the sensor surface, at a 1:1 magnification and without any intermediate zoom optics. (Other optical components may be present between the contact surface and the photosensitive semiconductor layer, such as the fiber optic faceplate described in U.S. Patent Application Publication No. 2017 / 0016829, previously incorporated by reference.) This sensing is analogous to making a "contact print" in film photography, where a photosensitive negative is placed in direct contact with photo paper and exposed to light. An image the same size as the negative is formed on the paper.

[0040] Reference again Figure 3 The area of ​​contact area image sensor 301 is preferably slightly larger than a typical footprint, for example, approximately 7 x 10 centimeters. In other embodiments, contact area image sensor 301 may be approximately 5 x 7 inches, 8 x 10 inches, or 9 x 12 inches, or another suitable size. Contact area image sensor 301 preferably includes thousands or even millions of pixels, which are small enough so that the digital image captured by contact area image sensor 301 provides a high-resolution representation of the target placed on the sensor. For example, each pixel may be approximately 130 square microns, or another suitable size. A 7 x 10 centimeter sensor with 130 micron pixels would have a total of approximately 414,000 pixels.

[0041] exist Figure 4 In FIG. 3 , the lid 202 has been opened and a target 401 in the form of a blot similar to blot 100 has been placed on the contact area image sensor 301. The target 401 includes a lane 402 of non-luminescent protein standards and a plurality of locations 403 that emit light by chemiluminescence, indicating the presence of a specific protein that has been separated in the experiment. Although in FIG. Figure 4 Only the top surface of the target 401 is visible, but chemiluminescent light is emitted from both sides and some of the light is directed downward toward the contact area image sensor 301.

[0042] Once target 401 is in place, cover 202 is closed. When cover 202 is in the closed position, it shields contact area image sensor 301 from ambient light. When cover 202 is in the closed position, a digital image can be captured if target 401 is used with contact area image sensor 301. To capture a digital image, image sensor 301 is flushed of accumulated charge and then read after a predetermined time, known as the exposure time. Furthermore, the act of reading contact area image sensor 301 takes a finite amount of time because charge must be transferred from the sensor (in a CCD sensor) or pixels must be read sequentially (in a CMOS sensor).

[0043] Generally speaking, the most chemiluminescent portions of target 401 will appear brighter in the digital image, and the less chemiluminescent or absent portions of target 401 will appear darker. The relative intensities of chemiluminescence can allow for approximate quantification of the amounts of different proteins present in a sample.

[0044] Figure 5 The figure shows a simplified block diagram of a CMOS image sensor 501 having 64 pixels 502. In practice, a CMOS sensor may have as many as hundreds of thousands or millions of pixels. Each pixel 502 includes a photosensitive area and multiple transistors (not shown). The transistors enable selection of individual pixels by a row selector 503 and a column selector 504, and enable the charge in the corresponding pixel to be converted into a voltage. The voltage of the selected pixel is presented to an analog-to-digital converter (ADC) 505 for conversion to a digital value, which is output from the sensor at 506. Under the control of timing logic 507, the pixel 502 can be reset (charge cleared), exposed for a predetermined time, and read out by the ADC 505. The resulting values ​​can be collected by an external computer and assembled into a digital image. CMOS sensors have the advantage that pixels can be read out individually and selectively from any part of the sensor; it is not necessary to read all pixels in the array. In addition, the ADC 505 can be formed on the same integrated circuit as the pixels 502 in the CMOS sensor.

[0045] Figure 6The figure shows a digital image 601 that can be captured from the target 401. The generation of the digital image 601 from the output of the contact area image sensor 301 can be accomplished in any suitable manner. For example, the imaging device 200 can include a controller that performs all necessary conversions and calculations and stores the digital image 601 in a standard image file format, such as JPEG (Joint Photographic Experts Group), TIFF (Tagged Image File Format), GIF (Graphics Interchange Format), PNG (Portable Network Graphics), or any other suitable standardized or proprietary format. In other embodiments, the signal can be passed from the imaging device 200 to a suitable computer system that converts the signal and generates the digital image file. Any feasible architecture and task division can be used.

[0046] Electronic array light sensors, such as contact area image sensor 301, have inherent limitations. For example, each pixel of an electronic array light sensor has a finite capacity for accumulating charge. As long as the amount of charge remains below the charge capacity of the pixel, the amount of charge is linearly proportional to the intensity of light falling on the pixel during the exposure time. However, when enough charge has accumulated to fill the pixel to capacity, any additional electrons generated overflow into the substrate of the sensor, and no additional charge is accumulated in the pixel. This condition is called saturation. When a pixel has been saturated, it is impossible to know the amount of light falling on the pixel other than noting that the light was enough to saturate the pixel. And when two pixels are saturated, it is impossible to know whether one of the two pixels may have received more light than the other pixel because the digital value read for both pixels will be the same. In other words, once saturation occurs, the response of the pixel is no longer linear.

[0047] Another inherent limitation of electronic array light sensors is noise. For example, even when a pixel is not exposed to light, it will accumulate a small amount of charge and may return a non-zero digital value when read. This is called dark noise. The amount of dark noise varies from pixel to pixel and is affected by the temperature of the pixel, as well as other factors. For each pixel, dark noise may have a relatively fixed component (called fixed-pattern dark noise), as well as a random component. Dark noise can make reading very low light intensities difficult because the signal generated by exposure to light can be drowned out by the dark noise. Many techniques have been developed to reduce or compensate for dark noise, including cooling the sensor, characterizing the dark noise as much as possible and subtracting the characterized noise from subsequent images, and other techniques.

[0048] Another type of noise inherent in electronic array light sensors is called shot noise. Shot noise arises from random variations in the number of photons collected by the sensor's pixels. Shot noise has an RMS value that is proportional to the square root of the image intensity, and therefore, the proportionality of shot noise to the desired signal is much more pronounced in low-light conditions than in bright-light conditions. (The ratio of a large number to its square root is greater than the ratio of a small number to its square root). Therefore, shot noise can also cause difficulties in reading low-light intensities. Due to its random nature, shot noise cannot be calibrated out. Techniques for dealing with shot noise can include capturing images with long exposure times or averaging multiple exposures.

[0049] Other noise sources exist in digital imaging, such as read noise. Generally speaking, the more light available for imaging, the less impact noise has on the final image. While fixed-pattern noise can be largely compensated for with proper calibration, random noise sources cannot.

[0050] These limitations—particularly pixel charge capacity and dark noise—determine the dynamic range of a particular sensor. Dynamic range indicates the range of image brightness that can be captured in a single exposure without saturation in bright areas and with enough signal in dark areas to distinguish between signal and noise.

[0051] Unfortunately, electrophoretic blots typically produce very weak light signals in some areas and very bright signals in other areas, so that the brightness range of the blot far exceeds the dynamic range of a typical electronic array light sensor, even when steps are taken to correct for noise as much as possible.

[0052] A technique sometimes used to handle a wide range of brightness is called high dynamic range (HDR) imaging. In HDR imaging, the same scene (such as a print) is exposed two or more times with different exposure times. For example, one exposure may be very short, so that even the brightest areas of the print do not saturate their corresponding sensor pixels. The second exposure may take a much longer exposure time. The chemiluminescent signal from the darkest parts of the print may be undetectable in the first short exposure because these signals are small relative to various noise sources. In the second, longer exposure, the signal from the darker areas may be visible and distinguishable from the noise, but the brightest locations may be saturated. The two exposures are mathematically combined to create an HDR image. For example, the value representing the brightest area in the first short exposure image can be multiplied by the ratio of the exposure times to estimate the value that would have been read for the bright pixels with the longer exposure time if the pixels were not saturated. In this way, the relative brightness of the bright and dark signals can be determined, even if accurately capturing both signals in a single exposure is impossible. In some applications, more than two exposures with graduated exposure times may be used. In some cases, at least some of the multiple exposures can be captured using non-destructive readout performed during a single, longer exposure. Such techniques are described in U.S. Patent Application Publication No. 2015 / 0172526, previously incorporated by reference.

[0053] However, even HDR imaging as described above may not completely eliminate saturation in some cases. For example, with a contact area image sensor (such as contact area image sensor 301), the minimum exposure time is determined by the finite amount of time it takes to read out an image from the sensor. Even if the sensor is reset and a readout is initiated immediately, some pixels may already be saturated by the time they are read. This problem can be exacerbated by higher-resolution sensors, which may take longer to read out than sensors with fewer pixels.

[0054] Embodiments of the present invention use selective reading of portions of the sensor to achieve shorter effective exposure times than when reading the entire sensor to avoid saturation.

[0055] The digital image 601 is displayed together with the attached annotations. Figure 7. Assume that the signal pixels corresponding to protein band 111 in region 701 are saturated, the signal pixels corresponding to protein band 112 in region 703 are relatively bright but unsaturated, and the signal pixels corresponding to protein band 113 in region 702 are very dark, so much so as to be largely indistinguishable from the noise present in this single-exposure image. Individual numerical values ​​for certain pixels are shown at 704. For example, if ADC 505 is an 8-bit converter and the system is designed so that pixel saturation corresponds to a full-scale reading of ADC 505, then all of the numerical values ​​in region 701 may be 255. The numerical values ​​in region 703 may be slightly less than 255, e.g., averaging approximately 200, and the numerical values ​​in region 702 may be very small, e.g., averaging only slightly greater than the values ​​in the surrounding dark areas of the image, which would contain only noise or background signal from the membrane. This example with an 8-bit ADC and a numerical range of 0-255 is merely an example. In other embodiments, a different number of bits may be used, for example, 10 bits, 12 bits, 16 bits, or other suitable numbers.In some embodiments, some digital images may be stored using floating point numbers.

[0056] Because the values ​​in region 701 are saturated and the values ​​in region 702 are not easily distinguished from noise, it is impossible to accurately quantify the relative brightness of the chemiluminescence in the corresponding region of target 401 .

[0057] Conventional HDR imaging may not be able to resolve this problem because the chemiluminescence corresponding to region 701 may be so bright that even reading out the sensor as quickly as possible may still result in pixel saturation.

[0058] According to an embodiment of the present invention, a digital image is captured using the entire electronic array photosensor, preferably as quickly as possible so that the image has the shortest possible exposure time, and the digital image is examined to see if it contains any saturated pixels. If not, the image can be satisfactorily used as a short-exposure image in an image sequence used in HDR imaging. However, if the image contains saturated pixels, the region containing the saturated pixel region is identified, and additional steps are performed to image the identified region without saturation.

[0059] Figure 8 The figure shows a flow chart of a method 800 according to an embodiment of the present invention. In step 801, a first digital image is captured. For example, the first digital image may include the entire electronic array photosensor and may be captured as quickly as possible. That is, the sensor is cleared of charge and readout is initiated immediately.

[0060] In step 802, a first digital image is examined to see if it includes any saturated pixels. For example (for an 8-bit system), the digital values ​​in the digital image can be individually checked to see if any are 255. If no saturated pixels are identified (or in some embodiments, only a negligible number of saturated pixels are identified), conventional or other HDR imaging can be used. The first digital image can be used as the image with the shortest exposure time in a series of digital images used in the HDR imaging process. In extreme cases, images can be captured with exposure times ranging from less than 1 millisecond to several minutes or more.

[0061] Although embodiments of the present invention are described as capturing digital images in order of increasing exposure time, this is not required. In other embodiments, the images may be captured in any order.

[0062] However, if saturated pixels are identified (or in some embodiments, a large number of saturated pixels are found), then a region of interest containing at least some of the saturated pixels is identified. Figure 7 , region 701 includes all of the saturated pixels in digital image 601. Region 701 is smaller than the entire digital image. In this example, region 701 only includes approximately three percent of the total area of ​​digital image 601.

[0063] In step 804, a second digital image is captured that contains only the region of interest. For example, the sensor is cleared of charge, and the pixels in the region of interest are read out and converted to numerical values. Because region 701 is much smaller than image 601, the second digital image can be read out much faster and with a much shorter exposure time. Therefore, in the second digital image, the pixels in region 701 may not be saturated.

[0064] Figure 9 The figure shows digital image 901 of area 701 that was captured more quickly than digital image 601. The respective values ​​of certain pixels in digital image 901 are shown at 902. For example, pixels that were saturated in digital image 601 now have a reading of approximately 120. The shorter exposure time of digital image 901 has eliminated the saturation of these pixels.

[0065] Reference again Figure 8 At step 806, the second digital image 901 is checked to see if it contains saturated pixels (in the examples so far, the second digital image 901 does not contain saturated pixels). If not, then all areas of the sample have been characterized within the linear range of the system, and at step 807, the captured images can be assembled into an HDR image. In some embodiments, digital images 601 and 901 may be sufficient to assemble an HDR image.

[0066] For example, assuming that the exposure time for digital image 601 is 50 milliseconds (a reasonable value for the time required to read out a sensor), and the exposure time for digital image 901 is 5 milliseconds), protein band 111 corresponding to region 701 receives approximately 24 digital counts per millisecond of exposure (120 counts / 5 milliseconds), while the slightly darker protein band 112 corresponding to region 703 receives approximately 4 digital counts per millisecond of exposure (200 counts / 50 milliseconds). Therefore, protein band 111 is approximately 6 times brighter than protein band 112.

[0067] In other words, if the system were not saturated, the pixel sensing bright protein band 111 would have obtained a value of approximately 1200 (24 counts / millisecond x 50 milliseconds) in digital image 601. Determining this relationship using digital image 601 alone is not possible.

[0068] Figure 10 Depicted is an HDR digital image 1001 assembled from a first digital image 601 and a second digital image 901, normalized to a 50 millisecond exposure time. The values ​​for regions 702 and 703 are taken from digital image 601, and the value in region 701 is calculated by multiplying the value from digital image 901 by the ratio of the exposure times of the two images. Because an HDR image is a mathematical construct rather than a single measurement, every value in the numeric value can be used as many bits as necessary, and "saturation" does not occur in the HDR image. In some embodiments, the HDR image can be stored using floating-point values ​​to accommodate a wide range of values.

[0069] In some embodiments, for example, if the protein in protein band 113 is not of interest, then HDR image 1001 may be the final result. However, in some embodiments, HDR imaging may be further performed ( Figure 8 808) to determine additional information about blot 100. For example, a third digital image of blot 100 may be taken with a longer exposure time to attempt to discern the relative brightness of protein band 113 compared to the other protein bands.

[0070] For example, Figure 11The figure shows another digital image 1101, which, in this example, is assumed to have an exposure time of 10 seconds, or 200 times the exposure time of digital image 601 (and 2000 times the exposure time of digital image 901). In this image, the pixels corresponding to protein band 111 (corresponding to region 701) and the pixels corresponding to protein band 112 (corresponding to region 703) are all saturated. In addition, the pixels corresponding to protein band 113 (corresponding to region 702) have accumulated a large amount of charge, and the overall background of the image has been slightly brightened due to the accumulation of charge from various noise sources. However, because protein band 113 has an actual chemiluminescent signal, the pixels corresponding to protein band 113 are proportionally brighter compared to the background, and the effect of random noise on the larger signal is less significant but visible. In this hypothetical example, the value corresponding to protein band 113 is, on average, about 59 counts higher than the background noise. Because the exposure time of the digital image is 10 seconds or 10,000 milliseconds, the protein band 113 corresponding to region 702 receives approximately 0.0059 digital counts per millisecond of exposure (59 counts / 10,000 milliseconds).

[0071] Thus, HDR imaging has been able to determine that the chemiluminescence brightness of protein band 111 is approximately 4067 times that of the chemiluminescence from protein band 113 (24 counts / millisecond / 0.0059 counts / millisecond). Using only digital images whose exposure times result in saturation of some pixels would not be possible to characterize this range of values. By reading the region of interest from the sensor only for certain bright pixels, the effective dynamic range of the system (including HDR imaging) is further extended.

[0072] In some cases, even the techniques described above may not be sufficient to eliminate saturation of all pixels. For example, for very bright chemiluminescent signals, even digital image 901 may be too large to read out before some of the pixels are saturated. That is, reference Figure 8 , the test in step 806 may determine that some pixels are still saturated.

[0073] In this case, the region of interest corresponding to the digital image 901 may be Figure 8 The step 809 is further subdivided. For example, Figure 12The figure shows a digital image 1201 similar to digital image 601, preferably taken with the shortest possible exposure time (e.g., an exposure time of 50 milliseconds) so that the entire sensor can be read. As shown at 1203, the pixels in area 1202 have been saturated. As before, a second digital image 1204 has been captured, which contains only the portion of the target corresponding to area 1202. Digital image 1204 can be captured much faster than digital image 1201, e.g., with an exposure time of 5 milliseconds, because it only uses a portion of the sensor. However, as Figure 12 As shown, the pixels in digital image 1204 are still saturated. That is, the chemiluminescence in the corresponding portion of the target is so bright that even a short exposure time of digital image 1204 cannot prevent saturation.

[0074] In this case, region 1202 can be further subdivided. For example, instead of using a region of interest that includes all of the saturated pixels in digital image 1201, discrete, continuous patches of saturated pixels can be isolated, such as in region 1205. The sensor is then reset and a third digital image 1206 is captured with an even shorter exposure time (e.g., 1 millisecond). Figure 12 In the example shown in FIG. 1 , without saturation, the third digital image has successfully read the pixels in region 1205, resulting in an average value of approximately 130. The brightness of the chemiluminescence corresponding to region 1205 can be characterized as approximately 130 counts per millisecond.

[0075] Reference again Figure 8 , the test at step 810 will indicate that saturation has been avoided, and as described above, the first digital image 1201, the second digital image 1204, and the third digital image 1206 can be assembled into an HDR image in step 811. The digital image can similarly capture other patches of saturated pixels in area 1205 and assemble them into an HDR image. As shown in step 812, further HDR imaging can be performed as needed.

[0076] The subdivision of the region of interest can be accomplished in any suitable manner. In the above example, each discrete patch of saturated pixels defines a separate region of interest, but this is not required. Region 1202 can be divided into two sub-regions, each including three patches for each of the saturated pixels, and having a read time of approximately half that of region 1202. Alternatively, region 1202 can be divided into three sub-regions, each including two patches for each of the saturated pixels, and having a read time of approximately one-third that of region 1202. The sub-regions can be selected without regard to the boundaries of any patch of saturated pixels. The sub-regions do not have to all be the same size and can be any portion of the region being subdivided.

[0077] And although the above figure shows two subdivisions of the digital image 1201, this is not a limitation. Figure 8 As shown, if the test at step 810 finds a saturated pixel, the subdivision may be repeated as many times as necessary and at a finer granularity to avoid saturation, including subdivisions down to and including sub-regions containing only one pixel, if necessary.

[0078] In some embodiments, the size of the sub-regions may be limited. For example, Figure 13 The figure illustrates a hypothetical print image 1301 in which widely dispersed portions of the image contain saturated pixels. In this case, a region of interest 1302 encompassing all of the saturated pixels in the image would be nearly as large as the digital image 1301 itself and would therefore require nearly as much time to read. For this reason, the size of the region of interest can be limited to a relatively small fraction of the divided area, e.g., no more than 30%, 40%, 50%, or other fraction of the divided area. For example, if the first image contains more than a limited percentage of saturated pixels, the first image can be arbitrarily divided into image regions below the size limit, e.g., into four or more regions of interest of equal size, or divided in some other manner.

[0079] exist Figure 12 In the example of FIG, a region of interest that is small enough to avoid saturation is iteratively identified. First, a region of interest 1202 is defined that includes all saturated pixels in the image, and when it is found that the region of interest 1202 is not small enough to avoid saturation, the region is further subdivided. In other embodiments, small regions of interest can be directly identified without performing such an iterative process.

[0080] For example, Figure 14 A flow chart of method 1400 according to such an embodiment is shown. Steps 1401, 1402, and 1403 of method 1400 are similar to steps 801, 802, and 803 of method 800 described above. At step 1401, a first image is captured using the entire sensor and preferably read out as quickly as possible. If no saturated pixels are found at step 1402, further imaging may be performed at step 1403.

[0081] However, if the first digital image includes saturated pixels, then at step 1404 the image area is divided into smaller regions of interest according to any suitable method. For example, the image area can be divided into a fixed number of regions of interest of equal size. Alternatively, each identifiable patch of saturated pixels can be designated as a region of interest. Any technique can be used, but preferably the technique divides the image area into regions of interest that are small enough so that the regions can be imaged much faster than the entire sensor.

[0082] For each of the identified regions of interest, an image of the region of interest is captured at step 1406. The resulting image is checked to see if it includes saturated pixels at step 1408. If so, and if further subdividing the region of interest is not possible or desirable, then that particular region may not be used for quantitative analysis, as shown at step 1407. In some embodiments, it may be possible to apply interpolation techniques or other estimation techniques to estimate the true brightness of saturated pixels.

[0083] Assuming the image of the current region of interest does not include saturated pixels, the image and an indication of its exposure time are stored at step 1409 , and control passes back to step 1406 .

[0084] Once all of the regions of interest have been processed, it may be desirable to re-image some of the regions. For example, any regions where pixels are not nearly saturated can be re-imaged with a longer exposure time to capture a digital image with a better signal-to-noise ratio. Adjacent regions of similar brightness can be grouped together for re-imaging.

[0085] In any case, the stored digital images from the region of interest may be assembled into an HDR image at step 1411. As shown in step 1412, other imaging may also be performed.

[0086] Compiling the HDR image may be performed in any feasible manner, but in some embodiments may be performed incrementally, as follows. Figure 15 The figure shows a digital image 1501 used as an example. Using the above-described techniques, two regions of interest 1502 and 1503 have been identified as containing saturated pixels. For the purpose of this example, the exposure time of the digital image 1501 is 50 milliseconds. Each of the regions 1502 and 1503 is small compared to the entire digital image 1501 and can be read more quickly. For example, Figure 16 The figure shows a digital image 1601 that was collected from a region of interest 1502 with an exposure time of 10 milliseconds. As is apparent, at the 10 millisecond exposure time, the pixels in region 1502 are no longer saturated and have an average value of approximately 150 digital counts. Digital image 1601 is stored for later use along with an indication of its 10 millisecond exposure time.

[0087] Figure 17A similar digital image 1700 is shown collected from region of interest 1503 with an exposure time of 10 milliseconds. It is apparent that the pixels are still saturated, so using the techniques described above, region 1503 is further subdivided, shown as regions of interest 1702, 1703, and 1704, so that each of the patches of saturated pixels can be imaged separately with even shorter exposure times.

[0088] Figure 18 The results of imaging regions 1702, 1703, and 1704 with a shorter exposure time are shown. Regions 1702 and 1703 were successfully imaged without saturation with an exposure time of 1 millisecond, but as shown, region 1704 required an exposure time of 0.5 milliseconds to avoid saturation. These images can be used for HDR imaging and are stored along with an indication of their exposure time.

[0089] To incrementally create an initial HDR image, an initial blank digital image 1901 may be first created, with all pixels set to zero intensity, e.g. Figure 19 The stored images of regions 1502 , 1702 , 1703 and 1704 are placed incrementally, starting with the image having the shortest exposure time—in this case, the image of region 1704 .

[0090] Next, if Figure 20 As shown, the digital image from region 1702 and the digital image from region 1703 are added. Because their exposure times are twice that of region 1704, the values ​​of the pixels in image 1901 corresponding to region 1704 are doubled in this process. In other words, all values ​​in the values ​​are normalized to the higher exposure time, which is currently 1 millisecond.

[0091] Finally, the digital image 1601 corresponding to the region 1502 is placed in the HDR image 1901, as shown in FIG. Figure 21 Because the exposure time of digital image 1601 is 10 times the exposure time used to capture areas 1702 and 1703, all of the existing numerical values ​​are multiplied by 10 to normalize them to a 10 millisecond exposure time.

[0092] Once the HDR image 1901 is constructed, accommodating the saturated pixels in the initial image 1501, the HDR image 1901 can be used as a basis for further HDR imaging. As additional images are taken at increasingly longer exposure times, the pixels in the region of interest in the HDR image 1901 can be further normalized to increasingly longer exposure times, with their relative brightness preserved throughout the process.

[0093] In other embodiments, the stored images (e.g., the unsaturated images of regions 1502, 1702, 1703, and 1704) can be examined to see which has the longest exposure time, and each image can be normalized and placed into the HDR image 1901 in a single step by normalizing its value directly to the longest exposure time.

[0094] The above example assumes that the imaging system is designed so that the system electronics have the same usable range as the sensor pixels, and that the saturation level of a pixel corresponds to the full-scale reading of the ADC (such as 255 for an 8-bit ADC). However, this is not required and may only be true in rare cases. For example, if the system electronics have a wider usable range than the pixels themselves, the saturation level of a pixel may be read as less than the full-scale reading of the ADC, such as 250 for an 8-bit ADC. In this case, identifying saturated pixels in the resulting digital image would mean identifying pixels with a value of 250 or more, rather than 255 (in this example).

[0095] Similarly, the system's electronics can have a narrower usable range than the pixel itself. For example, a charge amount less than saturation can be converted to a full-scale ADC reading. In this case, the ADC is saturated while the sensor pixel still has usable charge capacity. For the purposes of this disclosure, the term "saturated pixel" includes this case, as well as cases where the pixel's charge storage capacity is exceeded. The effect is similar, as when the ADC range is exceeded, the value read by the system is no longer linear with light intensity.

[0096] In some embodiments, a pixel can be identified as "saturated" when it exceeds an arbitrary brightness threshold, which can be somewhat lower than the pixel's charge capacity and lower than the full-scale ADC reading. For example, in a system with an 8-bit ADC, where actual pixel saturation is read as a value of 252, a pixel can be identified as saturated when its value exceeds a threshold of 245 or similar. This technique is more resilient to noise, temperature variations, etc.

[0097] The above embodiments have been explained in the context of such a sensor: the sensor can be globally reset and the sensor continues to accumulate charge until its next reset in a manner similar to the way a shutter is held open in a camera for a very long exposure. In other embodiments, some readouts can be performed using a "rolling shutter" mode, such as Figure 22As shown. In the sensor 2201, the rows 2202a, 2202b, 2202c, etc. are reset in sequence starting from the first edge 2203 of the sensor 2201. A very short time later (exposure time t), the rows are read out in sequence, also starting from the first edge 2203. Assuming that the exposure time t is shorter than the time required to read all the rows, the reset continues across the sensor before reading, and the distance of these read intervals depends on the exposure time, but can be as small as one row. In this way, the exposure time of each individual pixel can be much shorter than the time required to read the entire sensor. The pixels are not all exposed at the same time, but this has no harmful effect because the image being captured changes very slowly compared to the read time involved. Parts of the sensor 2201 that are smaller than the entire sensor can also be read in this way.

[0098] The above embodiments are mainly explained in the context of CMOS image sensors, in which individual pixels or at least rows of pixels can be read directly. In CMOS sensors, it is usually not necessary to read the entire sensor if only a small area is of interest.

[0099] In other embodiments, a CCD sensor can be used. CCDs are similar to CMOS sensors in that the pixels in the sensor accumulate charge proportional to the intensity of light falling on them. However, CCD sensors differ in how the charge is read. Instead of reading charge directly from individually addressable pixels (as in CMOS sensors), CCDs use a "bucket brigade" approach to move charge from the sensor and present it to a charge amplifier, which converts each charge into a voltage that can be digitized.

[0100] Figure 23 The figure shows a simplified schematic diagram of a CCD sensor 2301. The CCD sensor 2301 has a plurality of pixels 2302 arranged in an array of rows and columns. To read the CCD sensor 2301, the accumulated charge is transferred row by row into a transfer register 2303 and then transferred pixel by pixel through the transfer register 2303 to a charge amplifier 2304. The charge amplifier 2304 converts each charge into a voltage, which is then converted into a digital value by an analog-to-digital converter (ADC) 2305 and output digitally at 2306. For CCD sensors, the ADC 2305 is typically an external device and is not integrated into the sensor itself.

[0101] In CCD sensor 2301, individual pixels 2302 cannot be directly addressed and read. To measure the charge of any particular pixel, it must be transferred to charge amplifier 2304. However, not all charges need to be converted to numerical values. For example, to read pixels in a particular row 2307, the charge in rows below row 2307 can be transferred to transfer register 2303 and simply discarded immediately, such as being dumped onto the substrate of CCD sensor 2301, rather than being transferred to charge amplifier 2304. Once the charges from row 2307 arrive in transfer register 2303, they can be transferred to charge amplifier 2304 and converted. In this way, small areas of sensor 2301 can be read quickly. This technique can be used in embodiments of the invention using a CCD sensor to read small areas of interest as quickly as possible.

[0102] If the sites in the transfer register 2303 have a larger charge capacity than the pixels 2302, then the readout time can be further reduced by binning. For example, if the sites in the transfer register 2303 have a charge capacity at least twice that of the pixels 2302, then the charge from two rows of pixels 2302 can be transferred to the transfer register 2303 without the risk of saturating the transfer register sites. The charge in the transfer register 2303 can then be shifted out by the charge amplifier 2304 and converted to a numerical value by the ADC 2305. In this way, the sensor 2301 or a portion thereof is read at nearly twice the speed, but at the expense of reduced image resolution. If the charge storage sites in the transfer register 2303 have an even larger charge capacity, for example at least three times the charge capacity of the pixels 2302, then more rows can be binned, allowing for faster readout with less risk of saturating any one pixel 2302.

[0103] Sensor 2301 is a full-frame CCD sensor, in which its sensor area is substantially completely filled with light-sensitive pixels. Binning can also be used in interline transfer CCDs. In an interline transfer CCD, a portion of the imager area is occupied by charge storage sites that are shielded from light. Charge from the pixels can be transferred to the storage sites and read out during idle time. Binning can be used if the storage sites have a charge storage capacity greater than that of the pixels.

[0104] In other embodiments, a sensor with multiple taps may be used. In a multi-tap sensor, there are two or more readout paths that can operate in parallel, and therefore, readout can be performed at a speed approximately two times or more faster than a single-tap sensor. For example, in a CMOS sensor, two ADCs may be provided, with half of the pixels being routed to one of these ADCs for conversion, and the other half being routed to the other ADC for conversion. Any feasible number of taps may be provided.

[0105] Similar techniques can be used with CCD sensors. For example, two transfer registers similar to transfer register 2303 can be provided on opposite sides of the pixel array. Each of these transfer registers is provided with its own charge amplifier and ADC. Half of a row can be transferred to each of the two transfer registers, allowing the sensor to be read twice as fast. Similarly, any feasible number of taps can be used.

[0106] Depending on the sensor type and the sensor's readout design, the smallest readable area may be a single pixel row, a single pixel column, or even a single pixel.

[0107] Figure 24 The diagram illustrates an imaging system 2400 according to an embodiment of the present invention. Imaging system 2400 includes an electronic array light sensor 2401 and a computerized controller 2402. Controller 2402 further includes a processor 2403 and memory 2404. Memory 2404 stores instructions that, when executed by processor 2403, cause the system to perform embodiments of the present invention. Memory 2404 may also store other types of data, including image data.

[0108] The controller 2402 controls the sensor 2401 via one or more control signals 2405 and receives data from the sensor 2401 via one or more data signals 2406 .

[0109] Sensor 2401 may be a CMOS sensor, a CCD sensor, or another suitable type of sensor having pixels. Other elements of the system (such as a charge amplifier that may be present with a CCD sensor) are Figure 24 is omitted.

[0110] Any suitable architecture may be used for imaging system 2400. For example, control signal 2405 may preferably be a digital signal. Data signal 2406 may be a digital signal, for example, if sensor 2401 has one or more built-in analog-to-digital converters. In other cases, data signal 2406 may be an analog signal, and the conversion to a digital value may be performed in controller 2402. Many other variations are possible.

[0111] While embodiments of the present invention have been described in the context of CMOS and CCD sensors, it will be appreciated that the claims encompass the use of other kinds of sensors, including those yet to be developed.

[0112] In the appended claims, the terms "a" or "an" are intended to mean "one or more." The term "comprise" and its variations (such as "comprises" and "comprising"), when preceding the recitation of steps or elements, are intended to mean that the addition of other steps or elements is optional and is not excluded. The invention has now been described in some detail for purposes of clarity and understanding. However, one skilled in the art will appreciate that certain changes and modifications may be practiced within the scope of the appended claims.

Claims

1. A method for image capture, the method comprising: capturing a first digital image of the target using an electronic array light sensor; identifying one or more saturated pixels in the first digital image; identifying a first region of interest in the first digital image, the first region of interest containing at least some of the one or more identified saturated pixels; as well as capturing a second digital image of the target using the electronic array light sensor, the second digital image containing only the first region of interest, and the second digital image being captured with an exposure time shorter than an exposure time of the first digital image; identifying one or more saturated pixels in the second digital image; identifying a second region of interest containing at least some of the saturated pixels in the second digital image, the second region of interest being smaller than the first region of interest; as well as A third digital image of the object is captured using the electronic array light sensor, the third digital image containing only the second region of interest and being captured with an exposure time shorter than that of the second digital image.

2. The method according to claim 1, characterized in that The first digital image encompasses the entire electronic array light sensor, and the first digital image is read from the electronic array light sensor as quickly as possible.

3. The method according to claim 1, characterized in that The first region of interest includes all saturated pixels in the first digital image.

4. The method according to claim 1, characterized in that The first region of interest contains discrete patches of saturated pixels.

5. The method according to claim 4, characterized in that The first region of interest includes only one of at least two discrete patches of saturated pixels.

6. The method of any one of claims 1-5, further comprising assembling a high dynamic range digital image of the target using at least the first digital image and the second digital image.

7. The method of claim 6, further comprising: capturing a long exposure digital image of the target using the electronic array light sensor, the long exposure digital image being captured with an exposure time longer than an exposure time of the first digital image; as well as The high dynamic range digital image is assembled using at least the first digital image, the second digital image, and the long exposure digital image.

8. The method of claim 1, further comprising: A high dynamic range digital image of the object is assembled using at least the first digital image, the second digital image, and the third digital image.

9. The method of claim 1, further comprising: capturing a long exposure digital image of the target using the electronic array light sensor, the long exposure digital image being captured with an exposure time longer than an exposure time of the first digital image; as well as A high dynamic range digital image of the object is assembled using at least the first digital image and the long exposure digital image, wherein the second region of interest in the high dynamic range digital image includes data derived from the third digital image.

10. The method according to any one of claims 1 to 5, further comprising: identifying one or more saturated pixels in the second digital image; subdividing the first region of interest into one or more gradually smaller regions of interest; as well as One or more additional digital images of the one or more progressively smaller regions of interest are captured using progressively smaller exposure times until a digital image having no saturated pixels is obtained.

11. The method according to any one of claims 1 to 5, characterized in that The electronic array light sensor is a complementary metal oxide semiconductor (CMOS) sensor, and wherein capturing the second digital image of the object includes reading fewer than all pixels in the electronic array light sensor.

12. The method according to any one of claims 1 to 5, characterized in that The electronic array light sensor is a complementary metal oxide semiconductor (CMOS) sensor, and wherein capturing at least one of the first digital image and the second digital image includes using a rolling shutter.

13. The method according to any one of claims 1 to 5, characterized in that The electronic array light sensor is a charge coupled device (CCD) sensor, and wherein capturing the second digital image of the object includes transferring some charge from the CCD sensor and discarding it without converting it to a numerical value.

14. The method according to any one of claims 1 to 5, characterized in that The electronic array light sensor is a charge coupled device (CCD) sensor, and wherein capturing the second digital image of the target comprises binning charge in the CCD sensor.

15. The method of any one of claims 1-5, further comprising limiting a size of the first region of interest relative to the electronic array light sensor.

16. The method according to any one of claims 1 to 5, characterized in that The second digital image is captured at a lower resolution than the first digital image.

17. An imaging device comprising: An electronic array light sensor having a plurality of pixels; as well as a controller programmed to control the operation of the electronic array light sensor and to receive signals from the electronic array light sensor indicative of light intensities respectively falling on the pixels of the electronic array light sensor, wherein the controller is programmed to: capturing a first digital image of a target using the electronic array light sensor; identifying one or more saturated pixels in the first digital image; identifying a first region of interest in the first digital image, the first region of interest comprising one or more identified saturated pixels; as well as capturing a second digital image of the target using the electronic array light sensor, the second digital image containing only the first region of interest, and the second digital image being captured with an exposure time shorter than an exposure time of the first digital image; identifying one or more saturated pixels in the second digital image; identifying a second region of interest containing at least some of the saturated pixels in the second digital image, the second region of interest being smaller than the first region of interest; as well as A third digital image of the object is captured using the electronic array light sensor, the third digital image containing only the second region of interest and being captured with an exposure time shorter than that of the second digital image.

18. The imaging device according to claim 17, wherein The electronic array photosensor is a complementary metal oxide semiconductor (CMOS) sensor or a charge coupled device (CCD) sensor.

19. The imaging device according to claim 17, wherein: The controller is further programmed to construct a high dynamic range digital image of the object using at least the first digital image and the second digital image.

20. The imaging device according to any one of claims 17 to 19, characterized in that: The electronic array photosensor includes a plurality of taps.

Images