A method and system for adjusting microscopic image illumination based on flat-field correction

Abstract

This invention discloses a method and system for adjusting microscopic image illumination based on flat-field correction. By automating the process and combining the average response of multiple images, this method eliminates flat-field distortion caused by camera sensors. Combined with a background fitting method, this method effectively addresses image quality issues caused by uneven illumination, improving the reliability of microscopic images and the accuracy of subsequent analysis. This invention not only provides higher-quality image data for subsequent analysis of microscopic images but also promotes the development of microscopic imaging technology towards higher levels of automation and intelligence, contributing to in-depth exploration and application in fields such as biomedical research and materials science.

Description

Technical Field

[0001] The present invention belongs to the technical field of image flat-field correction and illumination adjustment, and in particular relates to a microscopic image illumination adjustment method and system design based on flat-field correction. Background Art

[0002] In microscopy, uniform illumination is crucial for image quality and subsequent analysis. Uneven illumination in microscopic images can lead to increased noise, reduced contrast, and overly bright or dark areas in the image, compromising feature extraction and quantitative analysis of cells, tissues, or materials. Therefore, flat-field correction and illumination adjustment for microscopic images are key challenges for researchers and engineers.

[0003] Flat-field correction and illumination adjustment, as core steps in digital image preprocessing, have important applications in fields such as microscopy, medical imaging, and industrial inspection. However, they still face several technical bottlenecks that need to be overcome:

[0004] (1) Limited flat-field calibration accuracy: The calibration quality of the flat-field image directly affects the correction effect. In actual operation, a reference image of a blank sample must be obtained under uniform light source illumination. However, the stability of the light source is affected by factors such as temperature drift and power supply fluctuations, which can easily lead to temporal fluctuations in the light intensity distribution. The spatial response differences such as the vignetting effect and lens coating non-uniformity of the optical system itself make the establishment of an ideal flat-field model challenging.

[0005] (2) Noise coupling interference: The dark current noise of the sensor follows a Poisson distribution and is particularly significant in low-light areas. Its nonlinear characteristics cause the traditional linear correction model to fail. Especially in CMOS devices, fixed pattern noise will produce spatial frequency coupling with flat-field distortion, forming residual artifacts.

[0006] (3) Insufficient adaptability to complex lighting scenarios: Flat-field correction technology can only address imaging unevenness caused by camera sensors. It has poor adaptability when processing high dynamic range images or multi-light source illumination. It cannot solve the problem of uneven illumination caused by external dynamic lighting conditions and the optical properties of the target object surface, thus destroying the authenticity and integrity of the image. In particular, in multi-light source interference scenarios, the correlation between the reflective characteristics of the object surface and the angle of the incident light leads to a shadow superposition effect, and conventional single-channel correction will produce chromatic distortion.

[0007] (4) Constraints on real-time processing efficiency: Flat-field correction involves the processing of a large amount of image data, especially in high-resolution microscopic images. The computational complexity is high and it is difficult to automate. How to optimize the algorithm and improve computational efficiency is also an important research direction.

[0008] Therefore, it is of great significance to propose a microscopic image illumination adjustment method based on flat-field correction. Summary of the Invention

[0009] The purpose of the present invention is to propose a method and system for microscopic image illumination adjustment based on flat-field correction, which can realize fully automatic, stable and efficient illumination adjustment of images with flat-field distortion and uneven illumination, and obtain high-quality uniformly illuminated images in a relatively short time.

[0010] The technical solution of the present invention is as follows: In a first aspect, the present invention provides a method for adjusting illumination of a microscopic image based on flat-field correction, comprising the following steps:

[0011] S1. Acquire multiple dark field noise images, and calculate the average response of each pixel in the dark field noise images as a first image.

[0012] S2. Acquire multiple bright field images, and calculate the average response of each pixel in the bright field images as the second image.

[0013] S3. Based on the first image and the second image, the original microscopic image is corrected using a flat field correction formula, and the correction result is used as the third image.

[0014] S4. Perform background estimation on the third image, and use the background estimation result as the fourth image.

[0015] S5. Calculate the mode of the fourth image.

[0016] S6. Determine the portion of the third image that needs to be subtracted from the brighter area by using the mode, and use the portion as the fifth image.

[0017] S7. Determine the portion of the dark area in the third image that needs to be added based on the mode, and use it as the sixth image.

[0018] S8. Calculate and obtain a lighting adjustment result according to the third image, the fifth image, and the sixth image.

[0019] Furthermore, step S1 includes the following sub-steps:

[0020] S11. After turning off the light source, obtain multiple dark field noise images of the camera sensor.

[0021] S12. Calculate the average response of each pixel in the dark field noise image as the first image:

[0022]

[0023] in represents the first image, represents pixel coordinates, Indicates the first A dark field noise image, Indicates the total number of dark field noise images captured by the camera sensor.

[0024] Furthermore, step S2 includes the following sub-steps:

[0025] S21. Place a reflective target with a uniform material and a width that covers the entire imaging field of view in the imaging field of view of the camera sensor.

[0026] S22. Adjust the lens aperture and illumination intensity of the camera sensor to obtain multiple bright field images without pixel saturation.

[0027] S23, calculating the average response of each pixel in the bright field image as the second image:

[0028]

[0029] in represents the second image, Indicates the first Zhang Liang field image, Indicates the total number of bright field images captured by the camera sensor.

[0030] Furthermore, the flat field correction formula in step S3 is:

[0031]

[0032] in represents the third image, represents the original microscopic image.

[0033] Furthermore, step S4 includes the following sub-steps:

[0034] S41. Calculate the size of the Gaussian filter kernel according to the size of the third image:

[0035]

[0036] in represents the size of the Gaussian filter kernel, Indicates the width of the third image, Indicates the height of the third image, Indicates rounding down.

[0037] S42. Perform Gaussian filtering on the third image based on the size of the Gaussian filter kernel to obtain a Gaussian filtering result:

[0038]

[0039]

[0040] in represents the Gaussian filtering result, represents the convolution operation, represents the third image, represents the Gaussian function, Indicates the coordinate offset of the Gaussian filter kernel relative to the center point, Represents the standard deviation of the Gaussian distribution.

[0041] S43, performing inversion processing on the Gaussian filtering result to obtain a background estimation result of the third image as the fourth image:

[0042]

[0043] in Indicates the fourth image.

[0044] Furthermore, the formula for calculating the mode of the fourth image in step S5 is:

[0045]

[0046]

[0047] in represents the mode of the fourth image, Represents grayscale value The number of pixels, represents the grayscale range of the image, Indicates that the The maximum grayscale value, and represents the size of the fourth image, Represents an indicator function, which takes the value 1 when the condition in the brackets is met, and takes the value 0 otherwise.

[0048] Furthermore, the calculation formula of the fifth image in step S6 is:

[0049]

[0050]

[0051] in represents the fifth image, Represents the difference between 255 and the mode.

[0052] Furthermore, the calculation formula of the sixth image in step S7 is:

[0053]

[0054] in Indicates the sixth image.

[0055] Furthermore, the calculation formula for the illumination adjustment result in step S8 is:

[0056]

[0057] in Indicates the lighting adjustment result.

[0058] In a second aspect, the present invention provides a microscopic image illumination adjustment system based on flat-field correction, which is configured to execute the above-mentioned microscopic image illumination adjustment method based on flat-field correction.

[0059] The present invention has the following beneficial effects: through an automated processing flow and the average response of multiple images, it eliminates flat-field distortion caused by camera sensors. Combined with background fitting methods, it effectively addresses image quality issues caused by uneven illumination, improving the reliability of microscopic images and the accuracy of subsequent analysis. This not only provides higher-quality image data for subsequent analysis of microscopic images, but also promotes the development of microscopic imaging technology towards higher levels of automation and intelligence, contributing to in-depth exploration and application in fields such as biomedical research and materials science. BRIEF DESCRIPTION OF THE DRAWINGS

[0060] Figure 1 FIG2 is a flow chart of a method for adjusting illumination of a microscopic image based on flat-field correction according to an embodiment of the present invention.

[0061] Figure 2 FIG2 is a schematic diagram showing a flat-field correction for a sample with uniform material according to an embodiment of the present invention.

[0062] Figure 3 Shown is a stitching result image without flat-field correction provided by an embodiment of the present invention.

[0063] Figure 4 Shown is a stitching result image after flat-field correction provided by an embodiment of the present invention.

[0064] Figure 5 FIG2 is a schematic diagram of an image with uneven illumination provided by an embodiment of the present invention.

[0065] Figure 6 FIG. 4 is a schematic diagram of Gaussian filtering results provided by an embodiment of the present invention.

[0066] Figure 7 FIG2 is a schematic diagram of background estimation results of an image with uneven illumination provided by an embodiment of the present invention.

[0067] Figure 8 Shown is a grayscale histogram of the background estimation result provided by an embodiment of the present invention.

[0068] Figure 9 FIG2 is a partial schematic diagram of a portion of an image with uneven illumination that needs to be subtracted where brighter areas are present in the image according to an embodiment of the present invention.

[0069] Figure 10 FIG. 1 is a schematic diagram showing a portion of a darker area in an image with uneven illumination that needs to be added according to an embodiment of the present invention.

[0070] Figure 11 FIG. 1 is a schematic diagram of an image adjustment result for uneven illumination provided by an embodiment of the present invention.

[0071] Figure 12 FIG. 1 is a schematic diagram of an uneven illumination image adjustment process provided by an embodiment of the present invention.

[0072] Figure 13 FIG. 1 is a schematic diagram of uneven illumination adjustment results based on flat-field correction provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0073] The exemplary embodiments of the present invention will now be described in detail with reference to the accompanying drawings. It should be understood that the embodiments shown and described in the accompanying drawings are merely exemplary and are intended to illustrate the principles and spirit of the present invention, rather than to limit the scope of the present invention.

[0074] Example 1:

[0075] The embodiment of the present invention provides a method for adjusting illumination of a microscopic image based on flat field correction, such as Figure 1 As shown, the following steps S1 to S8 are included:

[0076] S1. Acquire multiple dark field noise images, and calculate the average response of each pixel in the dark field noise images as a first image.

[0077] Step S1 includes the following sub-steps S11-S12:

[0078] S11. After turning off the light source, obtain multiple dark field noise images of the camera sensor.

[0079] In the embodiment of the present invention, the camera sensor is a shooting device in a microscopic imaging system.

[0080] S12. Calculate the average response of each pixel in the dark field noise image as the first image:

[0081]

[0082] in represents the first image, represents pixel coordinates, Indicates the first A dark field noise image, Indicates the total number of dark field noise images captured by the camera sensor.

[0083] S2. Acquire multiple bright field images, and calculate the average response of each pixel in the bright field images as the second image.

[0084] Step S2 includes the following sub-steps S21-S22:

[0085] S21. Place a reflective target with a width that can cover the entire imaging field of view and a uniform material in the imaging field of view of the camera sensor. In the embodiment of the present invention, the camera sensor is a color camera, and the reflective target is usually a white target.

[0086] S22. Adjust the lens aperture and illumination intensity of the camera sensor to obtain multiple bright-field images without pixel saturation (the minimum pixel value must be greater than 128).

[0087] S23, calculating the average response of each pixel in the bright field image as the second image:

[0088]

[0089] in represents the second image, Indicates the first Zhang Liang field image, Indicates the total number of bright field images captured by the camera sensor.

[0090] S3. Based on the first image and the second image, the original microscopic image is corrected using a flat field correction formula, and the correction result is used as the third image.

[0091] In the embodiment of the present invention, first, the original microscopic image Subtract dark field image , removing dark current and noise, the original microscopic image in the embodiment of the present invention is a flat field distortion image; then from the bright field image Subtract dark field image , and obtain the flat-field image with current and noise removed; finally, dividing the two, we can get the third image after flat-field correction:

[0092]

[0093] in Indicates the third image.

[0094] like Figure 2 As shown in Figure 1, ideally, when a camera sensor images an object with uniform brightness, the grayscale values ​​of all pixels in the image should theoretically be the same. However, in reality, the values ​​of each pixel often vary greatly (especially the pixel values ​​at the center and around the image). Figure 2The left side shows a flat-field distortion image. As can be seen from the image, uneven light intensity caused by factors such as objective lens phase difference makes the image appear uneven, with the center being brighter and the periphery being darker, which undermines the authenticity of the image. Figure 2 After the flat field correction, the flat field distortion image on the left can be obtained. Figure 2 The flat-field correction result on the right shows color and brightness consistency.

[0095] In the multi-field image stitching scenario, flat field distortion will cause obvious stitching shadows in the stitched image, making the stitching result look unnatural and affecting the accuracy of subsequent quantitative processing. A set of 4 rows and 5 columns of microscopic images collected under a 10x objective lens are stitched, such as Figure 3 As shown in the figure, without flat field correction, there are obvious stitching marks at the stitching of adjacent images of a single field of view, and the overall distribution is grid-like. After implementing the above flat field correction process, the stitching result is as follows Figure 4 As shown, the uniformity of light intensity distribution is significantly improved, the brightness jump in the splicing transition area is effectively eliminated, the illumination and visual consistency are presented, a reliable image basis is provided for subsequent quantitative analysis, and the effectiveness of the method of this embodiment is verified.

[0096] In digital microscopy systems, in addition to the inherent physical limitations of imaging devices, the optical properties of the target surface and external conditions can also lead to uneven illumination. This can manifest itself in the form of insufficient overall illumination, abnormal local illumination gradients, and interference from specular reflections. To address this uneven illumination, embodiments of the present invention, in addition to flat-field correction, further incorporate illumination adjustment techniques based on background fitting to mitigate the uneven illumination caused by both the target itself and external factors.

[0097] like Figure 5 As shown, there is an obvious local dark area above the image, while a local bright area appears on the right side of the image. This phenomenon of uneven lighting is relatively common in microscopic imaging, and is usually caused by factors such as uneven distribution of light sources, optical system characteristics, or sample surface reflection characteristics. Such non-uniform lighting problems will lead to contrast imbalance in local areas of the image, resulting in loss of dark details or overexposure of bright areas, thereby destroying the authenticity and integrity of the image. This distortion not only affects the human eye's intuitive observation of the image, but also causes significant interference to subsequent image processing and analysis work (such as target recognition, morphological analysis, quantitative measurement, etc.), reducing the accuracy and reliability of the results. Therefore, it is necessary to adjust the lighting of such unevenly illuminated images. The uneven lighting phenomena of images that can be processed by the embodiments of the present invention include but are not limited to Figure 5 The type of image shown.

[0098] S4. Perform background estimation on the third image, and use the background estimation result as the fourth image.

[0099] Step S4 includes the following sub-steps S41 to S43:

[0100] S41. Calculate the size of the Gaussian filter kernel according to the size of the third image:

[0101]

[0102] in represents the size of the Gaussian filter kernel, Indicates the width of the third image, Indicates the height of the third image, In the above formula, the addition of 1 is to ensure that the kernel size is an odd number.

[0103] against Figure 5 The image in the image has a width and height of 2448×2048. According to the calculation formula in step S41, the Gaussian filter kernel size is obtained. .

[0104] S42. Perform Gaussian filtering on the third image based on the size of the Gaussian filter kernel to obtain a Gaussian filtering result:

[0105]

[0106]

[0107] in represents the Gaussian filtering result, represents the convolution operation, represents the third image, represents the Gaussian function, Indicates the coordinate offset of the Gaussian filter kernel relative to the center point, Represents the standard deviation of the Gaussian distribution, which determines the width of the Gaussian filter kernel and the degree of image blur. The larger it is, the blurrier the image. is the radius of the kernel, which represents the range of the Gaussian filter kernel in each direction.

[0108] by Figure 5 The image shown is used as the third image, and the Gaussian filtering result obtained is as follows Figure 6 shown.

[0109] S43, performing inversion processing on the Gaussian filtering result to obtain a background estimation result of the third image as the fourth image:

[0110]

[0111] in Indicates the fourth image.

[0112] right Figure 6 The Gaussian filter result shown in the figure is inverted and the background estimation result is as follows: Figure 7 shown.

[0113] S5. Calculate the mode of the fourth image.

[0114] In the embodiment of the present invention, the grayscale histogram of the fourth image is counted to find the pixel value corresponding to its peak value, which is the desired mode:

[0115]

[0116]

[0117] in represents the mode of the fourth image, Represents grayscale value The number of pixels, Indicates the grayscale range of the image (usually corresponding to 8-bit grayscale image), Indicates that the The maximum grayscale value, and represents the size of the fourth image, Represents the indicator function, which takes 1 when the conditions in the brackets are met, otherwise it takes 0. By traversing all grayscale values , find satisfaction The gray value of the global maximum is the desired mode .

[0118] right Figure 7 The background estimation result image shown is plotted as a normalized histogram, as shown in Figure 8 As shown, the mode is obtained by solving the formula in step S5 .

[0119] S6. Determine the portion of the third image that needs to be subtracted from the brighter area by using the mode, and use the portion as the fifth image.

[0120] In the embodiment of the present invention, solving 255 and the mode The difference between , the Gaussian filter result minus , get the part of the bright area in the third image that needs to be subtracted as the fifth image , specifically:

[0121]

[0122]

[0123] S7. Determine the portion of the dark area in the third image that needs to be added based on the mode, and use it as the sixth image.

[0124] In the embodiment of the present invention, the mode is subtracted from the fourth image to obtain the portion of the dark area in the third image that needs to be added as the sixth image. , specifically:

[0125]

[0126] In order to make the correction process more intuitive, Figure 9 and Figure 10 Shows the results after automatic maximum contrast adjustment. Figure 9 correspond Figure 5 The part that needs to be subtracted in the brighter area clearly shows the distribution and intensity information of the over-illuminated area; Figure 10 Then the corresponding Figure 5 The part that needs to be added to the darker area intuitively reflects the compensation needs of the insufficiently illuminated area. Figure 9 and Figure 10 Comparing these two images can provide a more intuitive understanding of the uneven distribution of lighting and the specific treatment methods for bright and dark areas during the correction process, thereby providing a clear reference basis for subsequent lighting adjustments.

[0127] S8. Calculate and obtain a lighting adjustment result according to the third image, the fifth image, and the sixth image.

[0128] In the embodiment of the present invention, the calculation formula for the illumination adjustment result is:

[0129]

[0130] in Indicates the lighting adjustment result.

[0131] like Figure 11 Shown Figure 5 The lighting adjustment results for the shown image.

[0132] like Figure 12 Shown is based on Figure 5 The uneven illumination image shown shows the entire process of performing uneven illumination adjustment calculation in step S8.

[0133] The method described in the embodiments of the present invention is used to Figure 13 The first row of images to be corrected is processed. The image size is 3724×3724. The system runs on a hardware platform with 16GB of memory and an i5-8250U processor. The average processing time for a single image is 4.4 seconds.

[0134] Figure 13The second row of images shows the adjustment results obtained after processing using the method of an embodiment of the present invention. The overall average gradient of the original image and the processed result is compared, and the region of interest with uniform background brightness distribution is intercepted to calculate the structural similarity index. It is concluded that the average gradient of the processed result is slightly higher than that of the original image, and the structural similarity index between the regions of interest is greater than 0.97.

[0135] The above embodiment only describes the illumination adjustment method for a single-channel image. Flat-field correction and illumination adjustment for color images require first separating the three RGB color channels. The flat-field correction-based illumination adjustment method for microscopic images described in this invention is then applied to each channel to obtain processing results for each channel. The channel data is then normalized and recombined into a multi-channel color image. The color information of a multi-channel image is independently carried by different spectral components. This process of channel separation, single-channel calculations, and cross-channel fusion achieves overall optimization.

[0136] In summary, the embodiments of the present invention provide a method for adjusting illumination of microscopic images based on flat-field correction. Based on the sensor imaging process and imaging results, illumination adjustment is performed after flat-field correction is completed, effectively improving image quality and providing higher-quality image data for subsequent image recognition, analysis, and diagnosis.

[0137] Example 2:

[0138] An embodiment of the present invention provides a microscopic image illumination adjustment system based on flat-field correction, which is configured to execute the microscopic image illumination adjustment method based on flat-field correction in the first embodiment.

[0139] The system in the embodiment of the present invention can be an electronic device, including a memory, a processor, and a computer program stored in the memory and running on the processor, and the processor executes the program to implement some or all steps of the microscopic image illumination adjustment method based on flat-field correction as described in Example 1.

[0140] In an embodiment of the present invention, an electronic device may include: a processor, a memory, a bus and a communication interface. The processor, the communication interface and the memory are connected via a bus. The memory stores a computer program that can be run on the processor. When the processor runs the computer program, it executes some or all steps of the microscopic image illumination adjustment method based on flat-field correction provided in the aforementioned embodiment 1 of the present application.

[0141] The system in the embodiment of the present invention may also be a computer-readable storage medium storing a computer program, which, when executed, implements some or all of the steps of the microscopic image illumination adjustment method based on flat-field correction as described in Example 1.

[0142] The computer-readable storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), erasable and programmable read-only memory (EEPROM), erasable and programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk. The readable storage medium can be any available medium that can be accessed by a general-purpose or special-purpose computer. The readable storage medium is coupled to the processor so that the processor can read information from the readable storage medium and write information to the readable storage medium. The readable storage medium can also be an integral part of the processor. The processor and the readable storage medium can be located in an application-specific integrated circuit (ASIC). The processor and the readable storage medium can also exist as discrete components in the point de-registration system.

[0143] The embodiments of the present invention may be provided as methods, devices or computer program products. Therefore, the present invention may take the form of a completely hardware embodiment, a completely software embodiment or an embodiment combining software and hardware aspects. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code. With reference to the processes and / or block diagrams of the methods, devices (apparatus) and computer program products according to the embodiments of the present invention, it should be understood that each process and / or block diagram in the flow charts and / or block diagrams, as well as the combination of the flow charts and / or block diagrams, may be implemented by computer program instructions. These computer program instructions may be provided to a computer-readable memory of a general-purpose computer, a special-purpose computer, an embedded computer or other programmable data processing device that operates in a specific manner, so that the instructions stored in the computer-readable memory produce a manufactured product including an instruction device that implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operation steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions executed on the computer or other programmable device for implementing one or more processes and / or blocks in the flowchart. Figure 1 A step that specifies a function in one or more boxes.

[0144] Those skilled in the art will appreciate that the embodiments described herein are intended to help readers understand the principles of the present invention, and it should be understood that the scope of protection of the present invention is not limited to such specific descriptions and embodiments. Those skilled in the art can make various other specific variations and combinations based on the technical teachings disclosed in the present invention without departing from the essence of the present invention, and such variations and combinations are still within the scope of protection of the present invention.

Claims

1. A method for adjusting illumination of a microscopic image based on flat-field correction, characterized in that: The following steps are involved: S1, acquiring multiple dark field noise images, and calculating the average response of each pixel in the dark field noise images as a first image; S2, acquiring multiple bright field images, and calculating the average response of each pixel in the bright field images as a second image; S3, based on the first image and the second image, correcting the original microscopic image using a flat-field correction formula, and using the correction result as the third image; S4, performing background estimation on the third image, and using the background estimation result as the fourth image; S5. Calculate the mode of the fourth image; S6. Determine the portion of the third image that needs to be subtracted from the brighter area based on the mode, and use the portion as the fifth image. S7, determining the portion of the dark area in the third image to be added based on the mode, and using the portion as the sixth image; S8. Calculate and obtain a lighting adjustment result according to the third image, the fifth image, and the sixth image.

2. The microscopic image illumination adjustment method based on flat-field correction according to claim 1, characterized in that: The step S1 includes the following sub-steps: S11, obtaining multiple dark field noise images of the camera sensor after turning off the light source; S12. Calculate the average response of each pixel in the dark field noise image as the first image: in represents the first image, represents pixel coordinates, Indicates the first A dark field noise image, Indicates the total number of dark field noise images captured by the camera sensor.

3. The microscopic image illumination adjustment method based on flat-field correction according to claim 1, characterized in that: The step S2 comprises the following sub-steps: S21. Place a reflective target with a uniform material and a width that covers the entire imaging field of view in the imaging field of view of the camera sensor; S22, adjusting the lens aperture and illumination intensity of the camera sensor to obtain multiple bright field images without pixel saturation; S23, calculating the average response of each pixel in the bright field image as the second image: in represents the second image, represents pixel coordinates, Indicates the first Zhang Liang field image, Indicates the total number of bright field images captured by the camera sensor.

4. The microscopic image illumination adjustment method based on flat-field correction according to claim 1, characterized in that: The flat field correction formula in step S3 is: in represents the third image, represents pixel coordinates, represents the first image, represents the second image, represents the original microscopic image.

5. The microscopic image illumination adjustment method based on flat-field correction according to claim 1, characterized in that: The step S4 comprises the following sub-steps: S41. Calculate the size of the Gaussian filter kernel according to the size of the third image: in represents the size of the Gaussian filter kernel, Indicates the width of the third image, Indicates the height of the third image, Indicates rounding down; S42. Perform Gaussian filtering on the third image based on the size of the Gaussian filter kernel to obtain a Gaussian filtering result: in represents the Gaussian filtering result, represents pixel coordinates, represents the convolution operation, represents the third image, represents the Gaussian function, Indicates the coordinate offset of the Gaussian filter kernel relative to the center point, represents the standard deviation of the Gaussian distribution; S43, performing inversion processing on the Gaussian filtering result to obtain a background estimation result of the third image as the fourth image: in Indicates the fourth image.

6. The microscopic image illumination adjustment method based on flat-field correction according to claim 1, characterized in that: The formula for calculating the mode of the fourth image in step S5 is: in represents the mode of the fourth image, Represents grayscale value The number of pixels, Represents the grayscale range of the image, Indicates that the The maximum grayscale value, and represents the size of the fourth image, Represents an indicator function, which takes 1 when the conditions in the brackets are met, otherwise it takes 0. represents the fourth image, Represents pixel coordinates.

7. The microscopic image illumination adjustment method based on flat-field correction according to claim 1, characterized in that: The calculation formula of the fifth image in step S6 is: in represents the fifth image, represents pixel coordinates, represents the Gaussian filtering result, Represents the difference between 255 and the mode, represents the mode of the fourth image.

8. The microscopic image illumination adjustment method based on flat-field correction according to claim 1, characterized in that: The calculation formula of the sixth image in step S7 is: in represents the sixth image, represents pixel coordinates, represents the fourth image, represents the mode of the fourth image.

9. The microscopic image illumination adjustment method based on flat-field correction according to claim 1, characterized in that: The calculation formula of the illumination adjustment result in step S8 is: in Indicates the result of light adjustment. represents pixel coordinates, represents the third image, represents the fifth image, Indicates the sixth image.

10. A microscopic image illumination adjustment system based on flat-field correction, characterized in that: The microscopic image illumination adjustment system based on flat-field correction is configured to execute the microscopic image illumination adjustment method based on flat-field correction as described in any one of claims 1-9.

Images