Microscopic image illumination adjustment method and system based on flat field correction

Abstract

The invention discloses a microscopic image illumination adjustment method and system based on flat field correction, which eliminate flat field distortion caused by a camera sensor through an automatic processing flow in combination with average response of multiple images, and effectively solve the image quality problem caused by uneven illumination in combination with a background fitting method. And the reliability and subsequent analysis precision of the microscopic image are improved. According to the invention, not only is better image data provided for subsequent analysis of microscopic images, but also the microscopic imaging technology is promoted to develop towards the direction of higher automation and intelligence, and deep exploration and application in multiple fields of biomedical research, material science and the like are facilitated.

Description

Technical Field

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

[0002] In the field of microscopic imaging, illumination uniformity is crucial for image quality and subsequent analysis. Uneven illumination of microscopic images can lead to increased noise, reduced contrast, over-bright or over-dark regions in the image, affecting the feature extraction and quantitative analysis of cells, tissues, or materials. Therefore, performing flat-field correction and illumination adjustment on microscopic images has become an important challenge for researchers and engineers.

[0003] Flat-field correction and illumination adjustment, as the core links of digital image preprocessing, have important application values in fields such as microscopic imaging, medical imaging, and industrial inspection, but still face multiple technical bottlenecks that need to be broken through urgently:

[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 needs to be obtained under the illumination of a uniform light source. However, the stability of the light source is affected by factors such as temperature drift and power supply fluctuations, which easily cause temporal fluctuations in the light intensity distribution. Spatial response differences such as the vignetting effect and uneven lens coating in the optical system itself make it challenging to establish an ideal flat-field model.

[0005] (2) Noise coupling interference: The dark current noise of the sensor follows a Poisson distribution, which is particularly significant in low-light regions. Its non-linear characteristics cause the failure of traditional linear correction models. Especially in CMOS devices, fixed pattern noise will spatially frequency-couple with flat-field distortion, forming artifact residues.

[0006] (3) Insufficient adaptability to complex illumination scenarios: Flat-field correction technology can only handle the imaging non-uniformity caused by the camera sensor, and has poor adaptability in dealing with high-dynamic-range images or multi-light-source illumination situations. It cannot solve the illumination non-uniformity problems caused by external dynamic illumination conditions and the optical properties of the target object surface, destroying the authenticity and integrity of the image. Especially in multi-light-source interference scenarios, the correlation between the reflection characteristics of the object surface and the incident light angle leads to a shadow superposition effect, and conventional single-channel correction will cause chromatic distortion.

[0007] (4) Restriction of 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 relatively high, and it is difficult to achieve automation. How to optimize the algorithm and improve the 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 object of the present invention is to propose a method and system for adjusting the illumination of microscopic images based on flat-field correction, realizing fully automatic, stable and efficient illumination adjustment for flat-field distorted and unevenly illuminated images, and obtaining high-quality evenly illuminated images in a short time.

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

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

[0012] S2. Obtain 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, correct the original microscopic image using the flat-field correction formula, and take the correction result as the third image.

[0014] S4. Estimate the background of the third image, and take the background estimation result as the fourth image.

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

[0016] S6. Determine the part to be subtracted from the brighter regions in the third image through the mode, as the fifth image.

[0017] S7. Determine the part to be added to the darker regions in the third image through the mode, as the sixth image.

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

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

[0020] S11. Obtain multiple dark-field noise images of the camera sensor after turning off the light source.

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

[0022]

[0023] where represents the first image, represents the pixel coordinates, represents the th dark-field noise image captured by the camera sensor, represents the total number of dark-field noise images captured by the camera sensor.

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

[0025] S21. Place a reflective target with a width that can cover the entire imaging field of view of the camera sensor and a uniform material 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. Calculate the average response of each pixel in the bright-field image as the second image:

[0028]

[0029] where represents the second image, represents the th bright-field image captured by the camera sensor, represents the total number of bright-field images captured by the camera sensor.

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

[0031]

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

[0033] Further, 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] where represents the size of the Gaussian filter kernel, represents the width of the third image, represents the height of the third image, represents rounding down.

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

[0038]

[0039]

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

[0041] S43. Perform color inversion on the Gaussian filtering result to obtain the background estimation result of the third image as the fourth image:

[0042]

[0043] Where Represents the fourth image.

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

[0045]

[0046]

[0047] Where Represents the mode of the fourth image, Represents the gray value The number of pixels, Represents the image gray scale range, Represents finding the Maximum gray value, And Represents the size of the fourth image, Represents the indicator function, which takes 1 when the condition in the parentheses is satisfied, otherwise 0.

[0048] Furthermore, the formula for calculating the fifth image in step S6 is:

[0049]

[0050]

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

[0052] Furthermore, the formula for calculating the sixth image in step S7 is:

[0053]

[0054] Where Represents the sixth image.

[0055] Furthermore, the formula for calculating the light adjustment result in step S8 is:

[0056]

[0057] Among them represents the result of light intensity adjustment.

[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 beneficial effects of the present invention are as follows: Through an automated processing flow and by combining the average responses of multiple images, the present invention eliminates the flat-field distortion phenomenon caused by the camera sensor, and by combining the background fitting method, effectively solves the image quality problem caused by uneven illumination, and improves the reliability of microscopic images and the subsequent analysis accuracy. The present invention not only provides higher-quality image data for the subsequent analysis of microscopic images, but also promotes the development of microscopic imaging technology towards higher automation and intelligence, and helps to deeply explore and apply in multiple fields such as biomedical research and materials science. Description of the Drawings

[0060] Figure 1 Shown is a flowchart of a microscopic image illumination adjustment method based on flat-field correction provided by an embodiment of the present invention.

[0061] Figure 2 Shown is a schematic diagram of flat-field correction for a sample with uniform material provided by an embodiment of the present invention.

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

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

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

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

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

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

[0068] Figure 9 Shown is a schematic diagram of the part that needs to be subtracted from the brighter area in an image with uneven illumination provided by an embodiment of the present invention.

[0069] Figure 10 The following is a schematic diagram of the part that needs to be added to the darker area in the unevenly illuminated image provided by the embodiment of the present invention.

[0070] Figure 11 The following is a schematic diagram of the adjustment result of the unevenly illuminated image provided by the embodiment of the present invention.

[0071] Figure 12 The following is a schematic diagram of the adjustment process of the unevenly illuminated image provided by the embodiment of the present invention.

[0072] Figure 13 The following is a schematic diagram of the adjustment result of uneven illumination based on flat-field correction provided by the embodiment of the present invention. Detailed implementation manners

[0073] Now, exemplary embodiments of the present invention will be described in detail with reference to the accompanying drawings. It should be understood that the embodiments shown and described in the drawings are merely exemplary, intended to illustrate the principles and spirit of the present invention, and not to limit the scope of the present invention.

[0074] Embodiment 1:

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

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

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

[0078] S11. Obtain multiple dark-field noise images of the camera sensor after turning off the light source.

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

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

[0081]

[0082] where represents the first image, represents the pixel coordinates, represents the th dark-field noise image captured by the camera sensor, represents the total number of dark-field noise images captured by the camera sensor.

[0083] S2. Obtain 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 to S22:

[0085] S21. Place a reflective target with a width that can cover the entire imaging field of view of the camera sensor and a uniform material in the imaging field of view of the camera sensor. In the embodiment of the present invention, when the camera sensor is a color camera, 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 (it is necessary to ensure that the minimum pixel value is greater than 128).

[0087] S23. Calculate the average response of each pixel in the bright-field image as the second image:

[0088]

[0089] where represents the second image, represents the th bright-field image captured by the camera sensor, represents the total number of bright-field images captured by the camera sensor.

[0090] S3. Based on the first image and the second image, use the flat-field correction formula to correct the original microscopic image, and take the correction result as the third image.

[0091] In the embodiment of the present invention, first subtract the dark-field image from the original microscopic image to remove the dark current and noise. In the embodiment of the present invention, the original microscopic image is a flat-field distorted image; then subtract the dark-field image from the bright-field image to obtain a flat-field image with the current and noise removed; finally, divide the two to obtain the third image after flat-field correction:

[0092]

[0093] where represents the third image.

[0094] As Figure 2 shown, ideally, when the camera sensor images an object with uniform brightness, the gray values of all pixel points 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 the periphery of the image). Figure 2On the left is a flat-field distorted image. As can be seen from the image, the uneven light quantity caused by reasons such as objective lens aberration makes the image present an uneven imaging phenomenon where the central part is brighter and the peripheral part is darker, destroying the authenticity of the image. Figure 2 After the flat-field correction of the left flat-field distorted image, Figure 2 the flat-field correction result on the right can be obtained, showing color and brightness consistency.

[0095] In the multi-field-of-view image stitching scenario, flat-field distortion will cause extremely obvious stitching shadows in the resulting stitched image, making the stitching result look unnatural and affecting the accuracy of subsequent quantitative processing. Stitch a set of 4-row and 5-column microscopic images collected under a 10x objective lens. As Figure 3 shown, when no flat-field correction is performed, there are obvious stitching marks at the stitching positions of adjacent single-field images, presenting a grid distribution as a whole. After implementing the above flat-field correction process, the stitching result is as Figure 4 shown. The uniformity of the light intensity distribution is significantly improved, effectively eliminating the brightness jump in the stitching transition area, presenting illuminance and visual consistency, providing a reliable image base for subsequent quantitative analysis, and verifying the effectiveness of the method in this embodiment.

[0096] In a digital microscopy imaging system, in addition to the inherent physical limitations of the imaging device, affected by the optical properties of the target object surface and external conditions, uneven illumination is also likely to occur during imaging, mainly manifested in forms such as insufficient overall illuminance, abnormal local illuminance gradient, and specular reflection interference. In view of the above uneven illumination phenomenon, in the embodiments of the present invention, on the basis of flat-field correction, a light illumination adjustment technology based on background fitting is further combined to adjust the uneven illumination phenomenon brought by the target object itself and external factors.

[0097] As Figure 5 shown, there is an obvious local dark area above the image, while there is a local bright area on the right side of the image. This uneven illumination phenomenon is relatively common in microscopy imaging and is usually caused by factors such as uneven light source distribution, optical system characteristics, or sample surface reflection characteristics. Such non-uniform illumination problems will lead to contrast imbalance in local areas of the image, resulting in the loss of dark details or overexposure of bright areas, thus destroying the authenticity and integrity of the image. This kind of distortion will not only affect the intuitive observation of the image by the human eye but also cause 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 perform light illumination adjustment on such uneven illumination images. The uneven illumination phenomena of images that can be processed in the embodiments of the present invention include but are not limited to Figure 5 the image types shown.

[0098] S4. Estimate the background of 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] where represents the size of the Gaussian filter kernel, represents the width of the third image, represents the height of the third image, represents rounding down. The +1 operation in the above formula is to ensure that the kernel size is odd.

[0103] For Figure 5 the image in, with a width and height of 2448×2048, according to the calculation formula in step S41, the size of the Gaussian filter kernel is obtained as .

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

[0105]

[0106]

[0107] where represents the Gaussian filtering result, represents the convolution operation, represents the third image, represents the Gaussian function, represents the coordinate offset relative to the center point within the Gaussian filter kernel, represents the standard deviation of the Gaussian distribution, which determines the width of the Gaussian filter kernel and the image blurring degree, the larger, the more blurred the image. is the radius of the kernel, representing the range of the Gaussian filter kernel in each direction.

[0108] Taking the Figure 5 shown image as the third image, the obtained Gaussian filtering result is as shown in Figure 6 .

[0109] S43. Perform color inversion on the Gaussian filtering result to obtain the background estimation result of the third image as the fourth image:

[0110]

[0111] where represents the fourth image.

[0112] ForFigure 6 Perform color inversion on the shown Gaussian filtering result to obtain the background estimation result as Figure 7 shown.

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

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

[0115]

[0116]

[0117] where represents the mode of the fourth image, represents the number of pixels with grayscale value , represents the image grayscale range (usually corresponding to an 8-bit grayscale image), represents finding the grayscale value that makes the largest, and represent the size of the fourth image, represents the indicator function, which takes 1 when the condition in the parentheses is satisfied and 0 otherwise. By traversing all grayscale values , find the grayscale value that satisfies to obtain the global maximum, which is the required mode .

[0118] For the background estimation result image shown in Figure 7 , draw a normalized histogram as Figure 8 shown, and solve its mode according to the formula in step S5.

[0119] S6. Determine the part to be subtracted from the brighter region in the third image through the mode, and use it as the fifth image.

[0120] In the embodiment of the present invention, solve the difference between 255 and the mode , subtract from the Gaussian filtering result to obtain the part to be subtracted from the brighter region in the third image, and use it as the fifth image , specifically:

[0121]

[0122]

[0123] S7. Determine the part to be added to the darker area in the third image through the mode, and use it as the sixth image.

[0124] In the embodiment of the present invention, subtract the mode from the fourth image to obtain the part that needs to be added to the darker area in the third image, and use it as the sixth image. , specifically:

[0125]

[0126] To make the presentation of the correction process more intuitive, Figure 9 and Figure 10 show the results after automatic maximum contrast adjustment. Figure 9 Corresponding to Figure 5 the part that needs to be subtracted from the brighter area clearly shows the distribution of the over-illuminated area and its intensity information; Figure 10 while Figure 5 corresponds to Figure 9 and Figure 10 the part that needs to be added to the darker area, intuitively reflecting the compensation requirements for the under-illuminated area. Through

[0127] S8. Calculate the light 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 light adjustment result is:

[0129]

[0130] where represents the light adjustment result.

[0131] As Figure 11 shown is Figure 5 the light adjustment result of the image shown.

[0132] As Figure 12 shown is Figure 5 based on the uneven illumination image shown, demonstrating the entire process of calculating the uneven illumination adjustment in step S8.

[0133] Using the method described in the embodiment of the present invention to process Figure 13 the first row of the image to be corrected, with an image size of 3724×3724, running on a hardware platform with 16GB of memory and an i5-8250U processor, and the average processing time for a single image is 4.4 seconds.

[0134] Figure 13The second row of images shows the adjustment results obtained by using the method of the embodiment of the present invention. The overall average gradients of the original image and the processed result are compared, and the region of interest with uniform background brightness distribution is intercepted to calculate the structural similarity index. The conclusion is 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 embodiments only introduce the illumination adjustment method for single-channel images. For the flat-field correction and illumination adjustment of color images, it is necessary to first separate the RGB three color channels, and respectively execute the microscopic image illumination adjustment method based on flat-field correction described in the present invention for each channel to obtain the processing results of each channel. Subsequently, the data of each channel is recombined into a multi-channel color image through normalization processing. The color information of the multi-channel image is independently carried by different spectral components, and the overall optimization is achieved through the above process of channel separation, single-channel operation, and cross-channel fusion.

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

[0137] Embodiment 2:

[0138] The 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 Embodiment 1.

[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 on the memory and running on the processor. The processor executes the program to implement some or all of the steps of the microscopic image illumination adjustment method based on flat-field correction described in Embodiment 1.

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

[0141] The system in the embodiment of the present invention can also be a computer-readable storage medium. The computer-readable storage medium stores a computer program, and when the computer program is executed, some or all of the steps of the microscopic image illumination adjustment method described in Embodiment 1 are implemented.

[0142] The above 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 programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic disk or optical disk. The readable storage medium can be any available medium accessible 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 a component of the processor. The processor and the readable storage medium can be located in an application-specific integrated circuit (ASIC), or the processor and the readable storage medium can exist as discrete components in a point registration system.

[0143] Embodiments of the present invention can be provided as a method, apparatus, or computer program product. Therefore, the present invention can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present invention can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) that include computer-usable program code. It is described with reference to the flowcharts and / or block diagrams of methods, devices (apparatuses), and computer program products according to embodiments of the present invention. It should be understood that each flow and / or block in the flowcharts and / or block diagrams, and combinations of flows and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a general-purpose computer, a special-purpose computer, an embedded computer, or other programmable data processing devices and stored in a computer-readable memory that works in a specific manner, such that the instructions stored in the computer-readable memory produce a manufactured article including an instruction device that implements the functions specified in one flow Figure 1 in one or more flows and / or Figure 1 blocks or multiple blocks. These computer program instructions can also be loaded onto a computer or other programmable data processing device, such that a series of operation steps are performed on the computer or other programmable device to produce a computer-implemented process. Thus, the instructions executed on the computer or other programmable device provide steps for implementing the functions specified in one flow or more flows and / or Figure 1 blocks or multiple blocks in the flowchart.

[0144] Those of ordinary skill in the art will realize that the embodiments described herein are provided to assist the reader in understanding 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 statements and embodiments. Those of ordinary skill in the art can make various other specific deformations and combinations that do not depart from the essence of the present invention based on these technical revelations disclosed in the present invention, and these deformations and combinations are still within the scope of protection of the present invention.

Claims

1. A method for adjusting the illumination of microscopic images based on flat-field correction, characterized in that, It includes the following steps: S1. Obtain multiple dark-field noise images, and calculate the average response of each pixel in the dark-field noise images as the first image; S2. Obtain multiple bright-field images, and calculate the average response of each pixel in the bright-field images as the second image; S3. Based on the first image and the second image, use the flat-field correction formula to correct the original microscopic image, and take the correction result as the third image; S4. Perform background estimation on the third image, and take the background estimation result as the fourth image; S5. Calculate the mode of the fourth image; S6. Determine the part to be subtracted from the brighter region in the third image through the mode as the fifth image; S7. Determine the part to be added to the darker region in the third image through the mode as the sixth image; S8. Calculate the illumination adjustment result according to the third image, the fifth image, and the sixth image.

2. The method for adjusting the illumination of a microscopic image based on flat-field correction according to claim 1, wherein, The step S1 includes the following sub-steps: S11. Obtain 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 images as the first image: wherein represents the first image represents pixel coordinates represents the th dark field noise image captured by the camera sensor represents the total number of dark field noise images captured by the camera sensor 3. The method for adjusting the illumination of a microscopic image based on flat-field correction according to claim 1, wherein The step S2 includes the following sub-steps: 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; S22. Adjust the lens aperture and illumination intensity of the camera sensor to obtain multiple bright-field images without pixel saturation; S23. Calculate the average response of each pixel in the bright-field images as the second image: Among them represents the second image represents the pixel coordinates represents the Zhang Liang field image captured by the camera sensor represents the total number of bright field images captured by the camera sensor 4. The method for adjusting the illumination of a microscopic image based on flat-field correction according to claim 1, wherein, The flat-field correction formula in the step S3 is: wherein represents the third image, represents pixel coordinates, represents the first image, represents the second image, represents the original microscopic image.

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

6. The method for adjusting the illumination of a microscopic image based on flat-field correction according to claim 1, characterized in that, The mode calculation formula of the fourth image in the step S5 is: Among them represents the mode of the fourth image, represents the number of pixels with gray value represents the gray range of the image, represents finding the gray value that makes it the largest, and represents the size of the fourth image, represents the indicator function, which takes 1 when the condition in the parentheses is satisfied and 0 otherwise, represents the fourth image, represents the pixel coordinates.

7. The method for adjusting the illumination of a microscopic image based on flat-field correction according to claim 1, wherein The calculation formula of the fifth image in the step S6 is: Among them 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 method for adjusting the illumination of a microscopic image based on flat-field correction according to claim 1, wherein, The calculation formula of the sixth image in the step S7 is: Among them represents the sixth image represents pixel coordinates represents the fourth image represents the mode of the fourth image 9. The method for adjusting the illumination of a microscopic image based on flat-field correction according to claim 1, wherein The calculation formula of the illumination adjustment result in the step S8 is: Among them represents the light adjustment result represents the pixel coordinates represents the third image represents the fifth image represents 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 used to configure and execute the microscopic image illumination adjustment method based on flat-field correction as described in any one of claims 1-9.

Images