Microscope system, image processing method, and storage medium

Abstract

Provided is a technique for performing good black balance correction without being affected by color components of an observed image. A microscope system is provided with: a microscope that forms an observation optical image; an imaging device that captures an observation optical image and generates an observation image; a histogram generation unit that generates a pixel value histogram for each color component of the observation image; a correction value determination unit that determines a black balance correction value on the basis of the pixel value histogram for each color component; a correction unit that generates a corrected observation image by subtracting the black balance correction value from a pixel value for each color component of each pixel of the observation image; and an output device that outputs the corrected observation image.

Description

Technical Field

[0001] The disclosure of this specification relates to a microscope system, an image processing method, and a storage medium. Background Art

[0002] Fluorescence observation is known as one of the observation methods using a microscope. Fluorescence observation is an observation method that uses fluorescent dyes or fluorescent proteins to visualize specific sites or specific proteins or molecules in cells.

[0003] In fluorescence observation, due to the autofluorescence of the optical components (e.g., objective lens) included in the microscope, fluorescence is sometimes generated in the background other than the specimen in the observed image, and the contrast between the specimen and the background is reduced. Therefore, in order to prevent such a reduction in contrast, black balance correction is used. Black balance correction subtracts the brightness of the background (brightness caused by the autofluorescence of the optical components) from the observed image to clarify the difference in brightness between the specimen and the background, thereby obtaining an observed image with high contrast.

[0004] Patent document 1 discloses a microscope system that performs black balance correction. The microscope system of Patent document 1 generates a brightness distribution for an observed image, detects the brightest displacement point among the displacement points in the brightness distribution as a high brightness displacement point, detects the darkest displacement point as a low brightness displacement point, calculates the difference between the brightness value of the high brightness displacement point and the brightness value of the low brightness displacement point, determines the brightness value of the low brightness displacement point as a correction value based on the difference, performs correction by subtracting the correction value from the brightness value of each pixel constituting the observed image based on the determination result, and outputs (e.g., displays) the corrected observed image.

[0005] Prior art literature

[0006] Patent Literature

[0007] Patent Document 1: Japanese Patent Application Publication No. 2010-098719 Summary of the invention

[0008] Problems to be solved by the invention

[0009] In the microscope system of Patent Document 1, a correction value (black balance correction value) is determined based on the brightness distribution (brightness histogram) of an observed image.

[0010] However, in the microscope system of Patent Document 1, for example, when the observed image is a color image (an image in which each pixel has an R value, a G value, and a B value), when black balance correction is performed by calculating brightness information based on color information, the following problem occurs.

[0011] The brightness information (Y value) of each pixel of the observation image is obtained, for example, from the color information (R value, G value, B value) of the pixel by the following formula (1).

[0012] Y=0.299*R+0.587*G+0.114*B (1)

[0013] For example, when the object of observation is blue (B) monochromatic fluorescence, the red component (R value) and green component (G value) of each pixel of the observed image become values ​​close to 0, so the brightness information (Y value) of each pixel obtained by the above formula (1) is essentially multiplied by the minimum weight coefficient (0.114) for only the blue component (B value) of the pixel. As a result, the correction value determined based on the brightness histogram becomes a small value. When such a small correction value is used for black balance correction, the brightness of the background (brightness caused by the autofluorescence of the optical component) cannot be sufficiently subtracted from the observed image, and the reduction in the contrast between the specimen and the background in the observed image cannot be sufficiently improved.

[0014] An object of one aspect of the present invention is to provide a technique for performing good black balance correction without being affected by the color components of an observed image.

[0015] Means for solving problems

[0016] A microscope system according to one embodiment of the present invention comprises: a microscope that forms an observation optical image; an imaging device that captures the observation optical image to generate an observation image; a histogram generating unit that generates a pixel value histogram for each color component of the observation image; a correction value determining unit that determines a black balance correction value based on the pixel value histogram for each color component; a correction unit that subtracts the black balance correction value from the pixel value of each color component of each pixel of the observation image to generate a corrected observation image; and an output device that outputs the corrected observation image.

[0017] An image processing method according to one embodiment of the present invention performs the following processing by a computer: generating a pixel value histogram for each color component of an observation image, wherein the observation image is generated by photographing an observation optical image formed by a microscope with a camera device, determining a black balance correction value based on the pixel value histogram for each color component, and subtracting the black balance correction value from the pixel value of each color component of each pixel of the observation image to generate a corrected observation image.

[0018] A storage medium according to one embodiment of the present invention is a computer-readable storage medium storing a program, wherein the program causes a computer to perform the following processing: generating a pixel value histogram for each color component of an observation image, wherein the observation image is generated by capturing an observation optical image formed by a microscope with a camera device, determining a black balance correction value based on the pixel value histogram for each color component, and subtracting the black balance correction value from the pixel value of each color component of each pixel of the observation image to generate a corrected observation image.

[0019] Effects of the Invention

[0020] According to the above aspect, it is possible to perform good black balance correction without being affected by the color components of the observed image. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Figure 1 This is a diagram illustrating the configuration of a microscope system according to one embodiment.

[0022] Figure 2 This is a diagram for explaining an example of a part of the processing performed by the correction value determination unit.

[0023] Figure 3 This is a flowchart illustrating image processing performed by the image processing device.

[0024] Figure 4 This is a diagram illustrating the configuration of a microscope system according to Modification 1.

[0025] Figure 5 This is a diagram illustrating the structure of a microscope system according to Modification Example 2.

[0026] Figure 6 This is a diagram illustrating the structure of a microscope system according to Modification Example 3.

[0027] Figure 7 This is a diagram illustrating the hardware configuration of a computer. DETAILED DESCRIPTION

[0028] Hereinafter, embodiments of the present invention will be described with reference to the drawings.

[0029] Figure 1 This is a diagram illustrating the configuration of a microscope system according to one embodiment. Figure 2 This is a diagram for explaining an example of a part of the processing performed by the correction value determination unit.

[0030] Figure 1 The illustrated microscope system 1 includes a microscope 10 , an imaging device 20 , an image processing device 30 , and an output device 40 .

[0031] The microscope 10 forms an observation optical image of a specimen. The microscope 10 is a microscope capable of fluorescence observation, and forms an observation optical image during, for example, fluorescence observation.

[0032] The camera device 20 is provided at the microscope 10, and captures the observation optical image formed by the microscope 10 to generate an observation image. The camera device 20 has an image sensor such as a CCD (Charge Coupled Device) or a CMOS (Complementary Metal Oxide Semiconductor), and the image sensor has, for example, a Bayer arrangement color filter. The observation image generated by the camera device 20 may be an image in the form of a Bayer arrangement, or may be a color image after the Bayer arrangement image is subjected to development processing. The development processing includes processing for converting the Bayer arrangement image into a color image, such as de-mosaic processing, color processing, gamma correction processing, edge emphasis processing, and noise reduction processing.

[0033] The image processing device 30 performs a process for performing black balance correction on the observation image generated by the imaging device 20. The image processing device 30 includes a histogram generating unit 31, a correction value determining unit 32, and a correcting unit 33.

[0034] The histogram generation unit 31 generates a pixel value histogram for each color component of the observation image generated by the imaging device 20. For example, when the observation image generated by the imaging device 20 is an RGB color image (an image in which each pixel has color components of R, G, and B), three pixel value histograms of the R component, the G component, and the B component are generated. Alternatively, when the observation image generated by the imaging device 20 is an image in the Bayer arrangement format, four pixel value histograms of the R component, the Gr component, the Gb component, and the B component are generated.

[0035] In the following, the data of the histogram generated by the histogram generating unit 31 is expressed as Hist[c][n]. Here, c represents the color component, and n represents the signal level (pixel value). For example, when the observed image is an RGB color image and the signal level of each color component is expressed in 8 bits, c is any color component among R, G, and B, and n is any value between 0 and 255. In this case, for example, Hist[R]

[32] represents the number of pixels when the signal level of the R component of the RGB color image is 32.

[0036] The correction value determination section 32 determines the black balance correction value based on the pixel value histogram for each color component generated by the histogram generation section 31. Specifically, the correction value determination section 32 determines the black balance correction value as follows.

[0037] First, the correction value determination unit 32 determines, for each color component, a pixel value whose number of pixels accumulated from the darkest part of the pixel value histogram of the color component reaches a threshold value as a black balance correction value candidate. Specifically, based on the pixel value histogram data Hist[c][n] of each color component and the threshold Thr, the minimum x that satisfies the following equation (2) is obtained for each color component.

[0038]

[0039] The minimum x that satisfies the above formula (2) is Figure 2 As shown in the example, the number of pixels accumulated from the darkest part of the histogram ( Figure 2 The pixel value that reaches the threshold value Thr) is determined as a candidate for the black balance correction value. Hereinafter, x obtained for each color component using the above formula (2) is expressed as x[c]. Here, c represents the color component.

[0040] Next, the correction value determination unit 32 determines the maximum value among the black balance correction value candidates determined for each color component as the black balance correction value. Specifically, the black balance correction value X is determined by the following equation (3) or the following equation (4) based on x[c] for each color component. The following equation (3) is used when the observed image is an RGB color image, and the following equation (4) is used when the observed image is an image in the Bayer arrangement format.

[0041] X=MAX(x[R],x[G],x[B]) (3)

[0042] X=MAX(x[R],x[Gr],x[Gb],x[B]) (4)

[0043] Here, MAX() represents a function that returns the maximum value among arguments.

[0044] The reason why the maximum value among the black balance correction value candidates determined for each color component is determined as the black balance correction value is to prevent the correction section 33 from making the black balance correction of the observation image insufficient.

[0045] However, when the determined black balance correction value exceeds the correction upper limit value, the correction value determination unit 32 determines the correction upper limit value as the black balance correction value. That is, in this case, the black balance correction value is limited to the correction upper limit value. Specifically, the final black balance correction value Eval is determined by the following equation (5).

[0046] Eval=MIN(EvalMax,X) (5)

[0047] Here, EvalMax represents a correction upper limit value, and MIN() represents a function that returns the minimum value among independent variables.

[0048] The reason why the black balance correction value is limited to the correction upper limit value when the black balance correction value exceeds the correction upper limit value is to prevent excessive black balance correction of the observation image by the correction unit 33 .

[0049] The threshold Thr and the correction upper limit value may be fixed values ​​such as values ​​determined in consideration of the aesthetics of the observed image after black balance correction, or values ​​dynamically determined according to the ratio of the specimen to the background in the observed image, switching of the observation method, etc.

[0050] The correction unit 33 performs black balance correction on the observation image generated by the imaging device 20. Specifically, the correction unit 33 generates a corrected observation image by subtracting the black balance correction value determined by the correction value determination unit 32 from the pixel value of each color component of each pixel of the observation image. Specifically, the generation of the corrected observation image is performed as follows.

[0051] For example, if the observed image is an RGB color image, the pixel value of each color component of each pixel of the observed image is set to R(x, y), G(x, y), B(x, y), then the pixel value R'(x, y), G'(x, y), B'(x, y) of each color component of each pixel of the corrected observed image is calculated by the following formula (6).

[0052] R'(x,y)=R(x,y)-Eval

[0053] G'(x,y)=G(x,y)-Eval

[0054] B'(x,y)=B(x,y)-Eval (6)

[0055] In addition, Eval is a black balance correction value determined by the correction value determination unit 32 .

[0056] When the pixel value does not take a negative value, the following equation (7) is used instead of the above equation (6), and a process of limiting the negative pixel value to 0 is added.

[0057] R'(x,y)=MAX(0,R(x,y)-Eval)

[0058] G'(x,y)=MAX(0,G(x,y)-Eval)

[0059] B'(x,y)=MAX(0,B(x,y)-Eval) (7)

[0060] The case where the observed image is an RGB color image is as described above, but the generation of the corrected observed image can also be performed in the same manner when the observed image is an image in the Bayer arrangement format. In this case, the black balance correction value Eval is subtracted from the pixel value of each color component of each pixel in the Bayer arrangement format image (the pixel value of each R pixel, the pixel value of each Gr pixel, the pixel value of each Gb pixel, and the pixel value of each B pixel) (if the pixel value does not take a negative value, the negative pixel value is further limited to 0) to generate the corrected observed image.

[0061] The output device 40 outputs the calibration observation image generated by the calibration unit 33. The output device 40 is a display device such as a liquid crystal display, a printing device, etc. For example, the output device 40 as a display device displays the calibration observation image generated by the calibration unit 33.

[0062] Figure 3 This is a flowchart illustrating image processing performed by the image processing device.

[0063] exist Figure 3 In the illustrated image processing, when an observation image is input from the imaging device 20 to the image processing device 30, the histogram generation unit 31 generates a pixel value histogram for each color component of the input observation image (step S101). The input observation image is generated by capturing an observation optical image (for example, an observation optical image during fluorescence observation) formed by the microscope 10 by the imaging device 20. The details of the histogram generation by the histogram generation unit 31 are as described above.

[0064] Next, the correction value determination unit 32 determines a black balance correction value based on the pixel value histogram for each color component generated in step S101 (step S102 ). The details of how the correction value determination unit 32 determines the black balance correction value are also as described above.

[0065] Next, the correction unit 33 subtracts the black balance correction value determined in step S102 from the pixel value of each color component of each pixel of the input observation image to generate a corrected observation image (step S103). The details of how the correction unit 33 generates the corrected observation image are also as described above.

[0066] Then, the corrected observation image generated by the correction unit 33 is output from the image processing device 30 to the output device 40 , and is displayed by the output device 40 , which is, for example, a display device.

[0067] As described above, according to the present embodiment, black balance correction can be performed without calculating brightness information (calculation based on the above formula (1)), so that good black balance correction can be performed without being affected by the color components of the observed image. In addition, by such black balance correction, the brightness of the background (brightness caused by the autofluorescence of the optical component) can be sufficiently subtracted from the observed image during fluorescence observation, so that the reduction in the contrast between the specimen and the background in the observed image can be sufficiently improved.

[0068] This embodiment can also be configured as in the following modified examples. In the following modified examples, the same elements are denoted by the same reference numerals, and detailed description thereof is omitted.

[0069] <Modification 1>

[0070] Figure 4 FIG. 1 is a diagram illustrating the structure of a microscope system according to Modification Example 1. Figure 1 The microscope system 1 shown in the example Figure 4 In the microscope system 1 according to the illustrated modification example 1, the image processing device 30 further includes a developing unit 34 .

[0071] In the microscope system 1 according to the first modification, the observation image generated by the imaging device 20 is an image of the Bayer arrangement format, and accordingly, the corrected observation image generated by the correction unit 33 is also an image of the Bayer arrangement format.

[0072] The developing section 34 performs development processing including processing for converting the Bayer array image into a color image on the correction observation image generated by the correction section 33. The development processing includes, for example, demosaic processing, color processing, gamma correction processing, edge emphasis processing, and noise reduction processing.

[0073] In the microscope system 1 according to the first modification, the output device (eg, display device) 40 outputs (eg, displays) the corrected observation image developed by the developing section 34 .

[0074] According to Modification 1, since the black balance correction is performed before the development process, the development process can be performed on a good observation image after the black balance correction.

[0075] <Modification 2>

[0076] Figure 5 FIG. 2 is a diagram illustrating the structure of a microscope system according to a second modification. Figure 1 The microscope system 1 shown in the example Figure 5 In the microscope system 1 according to the illustrated modification example 2, the image processing device 30 further includes an image information reducing unit 35 .

[0077] The image information reduction unit 35 reduces the amount of information of the observation image generated by the camera device 20. The image information reduction unit 35 reduces the amount of information by reducing the observation image using, for example, known interpolation processing such as nearest neighbor interpolation (Nearest Neighbor) and linear interpolation (Bilinear). In addition, the image information reduction unit 35 may perform anti-aliasing by performing LPF (Low Pass Filter) processing on the observation image before the interpolation processing. In this way, it is possible to suppress the generation of aliasing that may occur with the reduction of the observation image caused by the interpolation processing. Alternatively, the image information reduction unit 35 may divide the observation image into predetermined block sizes, and use the average value of the pixel value of each color component of the pixels contained in each block size as the pixel value of each color component of one pixel, thereby reducing the observation image to reduce the amount of information.

[0078] According to the second modification, the histogram generation section 31 and the correction value determination section 32 are processed on the observation image whose information amount is reduced by the image information reduction section 35, so that it is possible to speed up the processing of the histogram generation section 31 and the correction value determination section 32. In addition, in the image information reduction section 35, although the observation image is reduced and the information amount is reduced, the influence on the shape of the histogram generated by the histogram generation section 31 is small, and therefore the accuracy of the black balance correction value determined by the correction value determination section 32 is not affected.

[0079] Modification 2 is particularly effective when the size of the observed image is large and the histogram is generated using hardware that performs parallel processing. Examples of hardware that performs parallel processing are the SIMD (Single Instruction Multiple Data) function of a DSP (Digital Signal Processor) and a CPU (Central Processing Unit). Generally speaking, the generation of a histogram is not suitable for parallel processing. Therefore, when using hardware that performs parallel processing to generate a histogram of an observed image of large size, the processing time becomes longer and real-time processing may become difficult. In contrast, in modification 2, a histogram is generated for an observed image that has been reduced in size and has a reduced amount of information, so the processing time does not become longer and real-time processing does not become difficult.

[0080] <Variation 3>

[0081] Figure 6 FIG. 2 is a diagram illustrating the structure of a microscope system according to a third modification. Figure 1 The microscope system 1 shown in the example Figure 6 In the microscope system 1 according to the illustrated modification 3, the image processing device 30 further includes a correction value stabilization unit 36 ​​.

[0082] In addition, in the microscope system 1 of variant example 3, the camera device 20 performs imaging (generation of observation images) at a specified frame rate, and for each frame, the image processing device 30 generates a corrected observation image for the observation image of the frame, and the output device 40 serving as a display device displays the corrected observation image.

[0083] The correction value stabilization unit 36 ​​smoothes the black balance correction value determined by the correction value determination unit 32 to stabilize the black balance correction value in the time direction. The correction value stabilization unit 36 ​​smoothes the black balance correction value by performing a moving average filter process such as a FIR (Finite Impulse Response) filter process or an IIR (Infinite Impulse Response) filter process, for example.

[0084] Furthermore, in the microscope system 1 according to the third modification, the correction section 33 generates a corrected observation image using the black balance correction value smoothed by the correction value stabilization section 36 .

[0085] According to Modification 3, for example, even when the black balance correction value determined by the correction value determination unit 32 varies due to the influence of noise when the imaging device 20 is performing imaging, the black balance correction value can be stabilized by smoothing performed by the correction value stabilization unit 36. As a result, in the calibration observation image generated by the correction unit 33 and displayed by the output device 40, the variation of brightness accompanying the variation of the black balance correction value can be suppressed, and thus the calibration observation image can be prevented from being difficult to observe due to the variation of brightness.

[0086] In Modification 3, when the black balance correction value Eval determined by the correction value determination unit 32 satisfies the following equation (8) with respect to the smoothed black balance correction value Eval′, the correction value stabilization unit 36 ​​may update the smoothed black balance correction value Eval′ by setting Eval′=Eval.

[0087] Eval<Eval'-h or Eval'+h<Eval(8)

[0088] Here, h may be a fixed value or a value dynamically determined according to the value of Eval'.

[0089] The case where the black balance correction value Eval satisfies the above-mentioned formula (8) is, for example, a case where the observation area is changed during observation and the content of the observed image changes. Even in such a case, it is possible to appropriately perform black balance correction in accordance with this.

[0090] Modifications 1 to 3 have been described above, but the present embodiment may be configured as a new modification in which two or more modifications among modifications 1 to 3 are combined.

[0091] In the microscope system 1 described above, the image processing device 30 may also be configured to Figure 7 The example is implemented by the computer 100. Figure 7 The diagram exemplifies the hardware configuration of the computer 100 .

[0092] Figure 7 The illustrated computer 100 includes a processor 101 , a memory 102 , a storage device 103 , a portable storage medium drive device 104 , a communication interface 105 , and an input / output interface 106 , which are connected to a bus 107 and can transmit and receive data to and from each other.

[0093] The processor 101 may be, for example, a single processor, or may be a multi-processor or a multi-core processor. The processor 101 performs various processes (for example, Figure 3 exemplified image processing).

[0094] The memory 102 includes a RAM (Random Access Memory) and a ROM (Read Only Memory). The RAM temporarily stores a part of the program executed by the processor 101. The RAM is also used as a working storage area for the processor 101. The ROM stores the program executed by the processor 101 and various data required for the execution of the program.

[0095] The storage device 103 is a device for storing data, and is a HDD (Hard Disk Drive), an SSD (Solid State Drive), or the like.

[0096] The portable storage medium drive device 104 drives the portable storage medium 104a, accesses its storage content, and reads and writes data. The portable storage medium 104a is a storage device, a floppy disk, an optical disk, an optical magnetic disk, etc. The portable storage medium 104a also includes a CD-ROM (Compact Disc Read Only Memory), a DVD (Digital Versatile Disc), a Blu-ray disc, a USB (Universal Serial Bus) memory, an SD card memory, etc.

[0097] The communication interface 105 is connected to a network by wire or wirelessly, and is an interface for communicating with an external device connected to the network.

[0098] The input / output interface 106 is connected to external devices (eg, the camera 20, the output device 40, and the input device) and is an interface for inputting and outputting data to and from the external devices. The input device is a keyboard, a mouse, a joystick, a touch panel, and the like.

[0099] In such a computer 100, the program executed by the processor 101 and various data required for the execution of the program are not limited to being stored in the memory 102, but may be stored in the storage device 103 or the portable storage medium 104a. In addition, the program executed by the processor 101 and various data required for the execution of the program may also be stored in the storage device 103 or the portable storage medium 104a from an external device connected to the network via the communication interface 105.

[0100] In addition, the computer 100 is not limited to Figure 7 The constituent elements shown as examples may also be configured to include a plurality of Figure 7 Some of the components described as examples may be configured by omitting some of the components.

[0101] In addition, the computer 100 may also be configured to include hardware such as a microprocessor, a DSP, an ASIC (Application Specific Integrated Circuit), a PLD (Programmable Logic Device), or an FPGA (Field-Programmable Gate Array). For example, the processor 101 may also be implemented using at least one of these hardware.

[0102] In passing Figure 7 When the illustrated computer 100 realizes the image processing device 30 , the functions of the histogram generating unit 31 , the correction value determining unit 32 , the correcting unit 33 , the developing unit 34 , the image information reducing unit 35 , and the correction value stabilizing unit 36 ​​are realized by the processor 101 .

[0103] As mentioned above, although embodiment and modification of this invention were described, this invention is not limited to the said embodiment and modification, Various improvements and changes can be made within the range which does not deviate from the summary of this invention.

[0104] Description of Reference Numerals

[0105] 1Microscope system

[0106] 10. Microscope

[0107] 20 Camera device

[0108] 30 Image processing device

[0109] 31 Histogram generation unit

[0110] 32 correction value determination unit

[0111] 33Correction Department

[0112] 34 Development Department

[0113] 35 Image Information Reduction Department

[0114] 36 calibration value stabilization unit

[0115] 40 Output device

[0116] 100 Computers

[0117] 101 Processor

[0118] 102 Memory

[0119] 103 Storage device

[0120] 104 Portable storage medium drive device

[0121] 104a Portable storage media

[0122] 105 Communication interface

[0123] 106 Input and Output Interfaces

[0124] 107 bus

Claims

1. A microscope system, characterized in that: The microscope system comprises: A microscope that forms an observation optical image; A camera device that captures the observation optical image to generate an observation image; a histogram generating unit configured to generate a pixel value histogram for each color component of the observed image; a correction value determination unit that determines a black balance correction value based on the pixel value histogram of each color component; a correction unit that generates a corrected observation image by subtracting the black balance correction value from a pixel value of each color component of each pixel of the observation image; and An output device outputs the corrected observation image.

2. The microscope system according to claim 1, characterized in that The correction value determination unit determines, for each of the color components, a pixel value for which the number of pixels accumulated from the darkest portion of the pixel value histogram of the color component reaches a threshold value as a black balance correction value candidate.

3. The microscope system according to claim 2, characterized in that: The correction value determination unit determines a maximum value among the black balance correction value candidates determined for each of the color components as the black balance correction value.

4. The microscope system according to claim 3, characterized in that When the determined black balance correction value exceeds an upper correction limit value, the correction value determination unit determines the upper correction limit value as the black balance correction value.

5. The microscope system according to any one of claims 1 to 4, characterized in that: The microscope system further includes a developing unit that performs a development process on the correction observation image, wherein the development process includes a process of converting the image in the Bayer arrangement form into a color image. The output device outputs the corrected observation image after the development process is performed by the developing section.

6. The microscope system according to any one of claims 1 to 4, characterized in that: The microscope system further includes an image information reduction unit that reduces the amount of information of the observed image. The histogram generating unit generates a histogram of pixel values ​​for each color component of the observation image whose information amount is reduced by the image information reducing unit.

7. The microscope system according to any one of claims 1 to 4, characterized in that: The microscope system further includes a correction value stabilization unit that smoothes the black balance correction value. The correction section generates the corrected observation image by subtracting the black balance correction value smoothed by the correction value stabilization section from the pixel value of each color component of each pixel of the observation image.

8. An image processing method, characterized in that: The following processing is performed by the computer: generating a pixel value histogram for each color component of an observation image generated by capturing an observation optical image formed by a microscope with an imaging device, determining a black balance correction value based on the pixel value histogram of each color component, The black balance correction value is subtracted from the pixel value of each color component of each pixel of the observation image to generate a corrected observation image.

9. A computer-readable storage medium storing a program, characterized in that: The program causes the computer to execute the following processing: generating a pixel value histogram for each color component of an observation image generated by capturing an observation optical image formed by a microscope with an imaging device, determining a black balance correction value based on the pixel value histogram of each color component, The black balance correction value is subtracted from the pixel value of each color component of each pixel of the observation image to generate a corrected observation image.

Images