CN108120680B - Method and device for removing stray light in microscopic imaging based on prior photoelectric characteristics - Google Patents

Abstract

The invention discloses a stray light removal method and device for microscopic imaging based on prior photoelectric characteristics, wherein the method includes: arranging a darkroom and taking points with logarithmic intervals on the exposure time parameter interval supported by the camera, and taking the first photo ;Only turn on the light source of the instrument in the darkroom, use light traps under the objective lens to absorb light, use the combination of camera exposure time and light source brightness, uniformly take points on the light source brightness, and take the same point at the camera exposure time, and take the second photo; Subtract the first photo from the second photo to eliminate the interference of the camera's own image sensor on the experimental results, and then obtain the change law of stray light in the exposure time of the camera and the brightness of the light source; analyze the intensity of stray light in a specific picture. When optimizing the distribution model in space, the stray light value of each pixel in the collected photos is obtained according to the change law, so as to remove the influence of stray light. This method can reduce the time complexity of the algorithm, and effectively improve the experimental efficiency and picture clarity.

Description

Method and device for removing stray light in microscopic imaging based on prior photoelectric characteristics

technical field

The invention relates to the fields of computer vision technology and image processing technology, in particular to a stray light removal method and device for microscopic imaging based on prior photoelectric characteristics.

Background technique

The multi-dimensional, multi-scale and high-resolution computational imaging instrument is currently the world's largest computational imaging instrument that takes into account "wide field of view, high resolution, and high frame rate". Images can realize multi-dimensional, multi-scale and high-resolution continuous observation. The instrument requires that the photos taken by the instrument have a lower signal-to-noise ratio while meeting the frame rate requirements, and can display more details, in order to clearly observe the life movement at the cell level, thereby helping experimenters to grasp the life process The effective information in it not only saves operation time (the biggest difficulty in biological experiments is that it is difficult to reproduce, because biological samples are very precious), but also can improve the accuracy of subsequent decoupling and reconstruction images, thus providing huge benefits for the field of life sciences. support.

In addition, the samples taken by the computational imaging instrument are mainly fluorescent samples. The fluorescent protein emits fluorescence under the excitation of light with a wavelength of 488, but the fluorescence intensity (effective information) is very low, and other interferences are relatively high. Therefore, it is very important to reduce the interference of the inherent characteristics of the instrument on the imaging of the sample during the imaging process. The most influential is the stray light that is not normally transmitted by the non-imaging light (mainly the light source light) in the optical path and reaches the surface of the image sensor. However, the experimental efficiency of related technologies is low, the picture definition is poor, and the algorithm is relatively complicated, which needs to be solved.

Contents of the invention

The present invention aims to solve one of the technical problems in the related art at least to a certain extent.

Therefore, an object of the present invention is to propose a method for removing stray light in microscopic imaging based on photoelectric characteristics prior, which can reduce the time complexity of the algorithm, effectively improve the experimental efficiency, and effectively improve the picture clarity.

Another object of the present invention is to propose a device for removing stray light in microscopic imaging based on prior photoelectric characteristics.

In order to achieve the above-mentioned purpose, an embodiment of the present invention proposes a stray light removal method based on photoelectric characteristic priori microscopic imaging, including the following steps: Step A1: Arranging a darkroom and setting the exposure time parameter range supported by the camera with Take logarithmically intermittent points and take the first photo; step A2: only turn on the light source of the instrument in the darkroom, use light traps under the objective lens to absorb light, combine the exposure time of the camera with the brightness of the light source, and uniformly obtain the brightness of the light source point, and take the same point as the step A1 on the camera exposure time, take a second photo; step A3: subtract the first photo from the second photo to eliminate the camera’s own image sensor to the experimental results interference, and then obtain the change law of stray light in the exposure time of the camera and the brightness of the light source; Step A4: When analyzing the optimal distribution model of stray light intensity in space for a specific picture, according to the change Regularly obtain the stray light value of each pixel in the collected photos to remove the influence of stray light.

The stray light removal method of microscopic imaging based on photoelectric characteristics prior in the embodiment of the present invention aims at the property that the optical path of the imaging instrument does not change in space within a period of time, and collects the stray light under different light source brightness and different camera exposure time Astigmatism data is analyzed, and complex modeling calculations are completed in the usual spare time, which reduces the time complexity of the real-time algorithm, and can generate the corresponding stray light spatial distribution model in real time according to the relevant parameters when taking photos. The influence of stray light is removed before image storage, thereby reducing the time complexity of the algorithm, not only effectively improving the experimental efficiency, but also effectively improving the image clarity.

In addition, the stray light removal method based on photoelectric characteristic prior microscopic imaging according to the above-mentioned embodiments of the present invention may also have the following additional technical features:

Further, in one embodiment of the present invention, the step A3 further includes: performing a median filter on the first photo and the second photo, and taking the middle half of the size of each picture The average gray value is used as the gray value of the photo, and a group of photos are linearly fitted when only the camera exposure time and the brightness of the light source are considered, so as to obtain the average gray value according to two sets of fitting coefficients. A binary linear function of the camera exposure time and the brightness of the light source.

Further, in one embodiment of the present invention, the step A4 further includes: Step 1: Separately extract each line of the photo for piecewise linear fitting, and divide it into three sections by using a threshold method, the threshold is The average gray value of the photo is multiplied by the correction coefficient, and the local maximum value of the peak signal-to-noise ratio is obtained by exhaustive search, and polynomial fitting is performed on each segment; Step 2: Take out each column of the photo separately, and repeat The operation of the step 1; step 3: taking the mean value of the two models as the final optimal distribution model of the stray light intensity in space, and combining the change rule to obtain the stray light value.

Furthermore, in one embodiment of the present invention, the polynomial of high degree in one variable is transformed into a multivariate linear function, and the least square method is used to accurately fit the coefficients of the polynomial to realize polynomial fitting.

Further, in one embodiment of the present invention, the formula for calculating the peak signal-to-noise ratio is:

Among them, MSE is the mean square error between the model and the original image, and n is the number of bits of the image.

In order to achieve the above purpose, another embodiment of the present invention proposes a stray light removal device for microscopic imaging based on photoelectric characteristics a priori, including: a first shooting module for arranging a darkroom and setting the exposure time parameters supported by the camera Take points with logarithmic intervals on the interval, and take the first photo; the second shooting module is used to turn on only the light source of the instrument in the dark room, and use a light trap to absorb light under the objective lens, and combine the exposure time of the camera with the brightness of the light source, and then The brightness of the light source is evenly selected, and the exposure time of the camera is the same as that of the step A1, and the second photo is taken; the elimination module is used to subtract the first photo from the second photo to obtain Eliminate the interference of the camera's own image sensor on the experimental results, and then obtain the change law of stray light in the exposure time of the camera and the brightness of the light source; the processing module is used to analyze the intensity of stray light in a specific image in space When optimizing the distribution model above, the stray light value of each pixel in the collected photos is obtained according to the change rule, so as to remove the influence of stray light.

The stray light removal device for microscopic imaging based on photoelectric characteristics prior in the embodiment of the present invention aims at the property that the optical path of the imaging instrument does not change in spatial position within a period of time, and collects the stray light under different light source brightness and different camera exposure time in advance. Astigmatism data is analyzed, and complex modeling calculations are completed in the usual spare time, which reduces the time complexity of the real-time algorithm, and can generate the corresponding stray light spatial distribution model in real time according to the relevant parameters when taking photos. The influence of stray light is removed before image storage, thereby reducing the time complexity of the algorithm, not only effectively improving the experimental efficiency, but also effectively improving the image clarity.

In addition, the stray light removal device based on photoelectric characteristic prior microscopic imaging according to the above-mentioned embodiments of the present invention may also have the following additional technical features:

Further, in one embodiment of the present invention, the elimination module is also used to perform median filtering on the first photo and the second photo, and take the average value of the middle half size of each picture The gray value is used as the gray value of the photo, and a group of photos is linearly fitted when only the camera exposure time and the brightness of the light source are considered, so as to obtain the average gray value according to the two sets of fitting coefficients. A quadratic linear function of the exposure time of the camera and the brightness of the light source.

Further, in one embodiment of the present invention, the processing module is also used to separately extract each line of the photo for piecewise linear fitting, and divide it into three segments by using a threshold method, where the threshold is the average gray value of the photo Degree value multiplied by the correction coefficient, the local maximum value of peak signal-to-noise ratio is obtained by exhaustive search method, polynomial fitting is performed on each segment, and each column of the photo is taken out separately, and the above operation is repeated, and the obtained The mean value of the two models is used as the final optimal distribution model of the stray light intensity in space, and the value of the stray light is obtained by combining the change rule.

Furthermore, in one embodiment of the present invention, the polynomial of high degree in one variable is transformed into a multivariate linear function, and the least square method is used to accurately fit the coefficients of the polynomial to realize polynomial fitting.

Further, in one embodiment of the present invention, the formula for calculating the peak signal-to-noise ratio is:

Among them, MSE is the mean square error between the model and the original image, and n is the number of bits of the image.

Additional aspects and advantages of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention.

Description of drawings

The above and/or additional aspects and advantages of the present invention will become apparent and easy to understand from the following description of the embodiments in conjunction with the accompanying drawings, wherein:

1 is a flowchart of a stray light removal method based on photoelectric characteristic prior microscopic imaging according to an embodiment of the present invention;

2 is a flow chart of a stray light removal method based on photoelectric characteristic prior microscopic imaging according to a specific embodiment of the present invention;

FIG. 3 is a schematic structural diagram of a stray light removal device based on photoelectric characteristic prior microscopic imaging according to an embodiment of the present invention.

Detailed ways

Embodiments of the present invention are described in detail below, examples of which are shown in the drawings, wherein the same or similar reference numerals designate the same or similar elements or elements having the same or similar functions throughout. The embodiments described below by referring to the figures are exemplary and are intended to explain the present invention and should not be construed as limiting the present invention.

The stray light removal method and device based on photoelectric characteristic prior microscopic imaging according to the embodiments of the present invention will be described below with reference to the accompanying drawings. Stray light removal methods for imaging.

FIG. 1 is a flow chart of a method for removing stray light in microscopic imaging based on prior photoelectric characteristics according to an embodiment of the present invention.

As shown in Figure 1, the stray light removal method based on photoelectric characteristic prior microscopic imaging includes the following steps:

Step A1: Arrange the darkroom and take the logarithmic intervals on the exposure time parameter interval supported by the camera, and take the first photo.

It can be understood that, as shown in Figure 2, the darkroom is arranged to eliminate any light source, and the photos are taken with logarithmic intervals on the exposure time parameter interval supported by the camera.

For example, arrange a dark room to avoid any light source, including visible light and invisible light, such as the infrared emitting device of an infrared camera, so it is necessary to control the camera to take pictures outside the dark room. Take points with logarithmic intervals on the exposure time parameter interval supported by the camera, and take photos (these photos are mainly dark current noise with small numerical changes).

Step A2: Only turn on the light source of the instrument in the darkroom, use a light trap to absorb light under the objective lens, and use the combination of camera exposure time and light source brightness to uniformly take points on the light source brightness, and the camera exposure time is the same as the point taken in step A1. Take a second photo.

It can be understood that, as shown in Figure 2, only the instrument light source is turned on in the darkroom, light traps are used under the objective lens to absorb light, and the exposure time of the camera is combined with the brightness of the light source to uniformly take points on the brightness of the light source, while the camera exposure time Take the same point as A1, take a photo.

Specifically, only the instrument light source is turned on in a darkroom, and light traps are used below the objective lens to absorb the emitted light. Dichroic mirrors and dozens of lenses are used in this instrument, so stray light comes mainly from the light source rather than light reflected back from the sample. Combining the exposure time of the camera and the brightness of the light source, the brightness of the light source is evenly selected, and the exposure time of the camera is the same as that of A1, and the photos are taken (these photos contain both stray light and dark current noise like A1).

Step A3: Subtract the first photo from the second photo to eliminate the interference of the camera's own image sensor on the experimental results, and then obtain the change law of stray light in the exposure time of the camera and the brightness of the light source.

It can be understood that, as shown in Figure 2, subtracting the photo in step A1 from the photo in step A2 can eliminate the interference of the camera's own image sensor on the experimental results, and then analyze the change law of stray light in the exposure time of the camera and the brightness of the light source.

Further, in one embodiment of the present invention, step A3 further includes: performing median filtering on the first photo and the second photo, and taking the average gray value of the middle half of each picture as the photo The gray value of the gray value, respectively, in the case of only considering the exposure time of the camera and the brightness of the light source, a linear fitting is performed on a group of photos, so as to obtain the binary primary value of the average gray value with respect to the exposure time of the camera and the brightness of the light source according to the two sets of fitting coefficients function.

It is understandable that, as shown in Figure 2, in step A3, the photos after A2-A1 are firstly subjected to median filtering (the photos taken by the camera under high exposure conditions will have step noise), and then the middle of each picture is taken. The average gray value of half the size is used as the gray value of the photo, and a group of photos are linearly fitted in the case of only considering the camera exposure time and the brightness of the light source, and finally according to two sets of fitting coefficients, a Binary linear function of average gray value with respect to camera exposure time and light source brightness.

Specifically, the photos taken by the camera under high exposure conditions will have step noise, that is, the gray value of the changed point is much larger than that of the surrounding points. Therefore, performing median filtering first can get very good results. It can be considered that the middle position of the camera image sensor can basically receive the light from the outside world, so the average gray value of the middle half of each processed picture is taken as the gray value of the photo. A group of photos are linearly fitted while only considering the exposure time of the camera and the brightness of the light source. Finally, a binary linear function of the average gray value with respect to the exposure time of the camera and the brightness of the light source can be obtained according to multiple sets of fitting coefficients.

Step A4: When analyzing the optimal distribution model of stray light intensity in space for a specific picture, the stray light value of each pixel in the collected photo is obtained according to the variation law, so as to remove the influence of stray light.

It can be understood that, as shown in FIG. 2, the embodiment of the present invention can analyze the optimal distribution model of stray light intensity in space for a specific picture, and combine the stray light in step A3 with the change law of camera exposure time and light source brightness. The stray light value of each pixel in the collected photos can be obtained, and then the influence of stray light can be removed accurately and in real time.

Further, in one embodiment of the present invention, step A4 further includes: Step 1: Separately extract each line of the photo for piecewise linear fitting, and divide it into three segments by using a threshold method, where the threshold value is The average gray value is multiplied by the correction coefficient, and the local maximum value of the peak signal-to-noise ratio is obtained by exhaustive search, and polynomial fitting is performed on each segment; Step 2: Take out each column of the photo separately, and repeat the operation of Step 1 ; Step 3: Take the mean value of the two models as the final optimal distribution model of stray light intensity in space, and combine the change rule to obtain the stray light value.

Optionally, in one embodiment of the present invention, the polynomial of higher degree in one variable is transformed into a multivariate linear function, and the least square method is used to accurately fit the coefficients of the polynomial to realize polynomial fitting.

It can be understood that in step A4, instead of directly finding a binary function that fits the model, the fitting is performed by row and column respectively, which is divided into three steps. Step 1: Take out each line of the photo separately, perform piecewise linear fitting, and divide it into three sections by thresholding. The threshold is the average gray value of the photo multiplied by a correction coefficient, and find the best one by exhaustive search. The fitting effect, that is, the local maximum value of PSNR (peak signal-to-noise ratio), performs polynomial fitting on each segment. This fitting is essentially a multiple linear regression problem, assuming Y=b ₀ +b ₁ X ₁ + b ₂ X ₂ +b ₃ X ₃ +b ₄ X ₄ +… where X ₁ =x, X ₂ =x ² , X ₃ =x ³ , X ₄ =x ⁴ ...X _n =x ⁿ Degree polynomials are transformed into multivariate linear functions, and polynomial coefficients can be accurately fitted using the least squares method. Step 2: Take out each column of the photo separately, and repeat the operation of step 1. Step 3: In order to ensure the continuity of the fitting model as much as possible, take the mean value of the two models as the final optimized distribution model of stray light intensity in space. Combining the stray light of A3 with the camera exposure time and the change law of light source brightness, the stray light value of each pixel in the collected photos can be obtained, and then the influence of stray light can be removed accurately and in real time.

Optionally, in one embodiment of the present invention, the formula for calculating the peak signal-to-noise ratio is:

Among them, MSE is the mean square error between the model and the original image, and n is the number of bits of the image.

Specifically, the optimal distribution model of stray light intensity in space is analyzed for a specific image. Generally, in order to ensure that the camera can capture 100% of the light entering the camera, the image sensor will be made larger than the actual incident light area, so the gray value of the edge of each photo actually taken will drop sharply. It is very difficult to directly find a suitable binary function model. Step 1: Take out each row of the photo separately, perform piecewise linear fitting, and divide it into three sections by thresholding. The threshold is the average gray value of the photo Multiplied by a correction coefficient ξ, the correction coefficient can be adjusted in the experiment to achieve the best fitting effect, and polynomial fitting is performed on each segment. This fitting is essentially a multiple linear regression problem.

Suppose Y=b ₀ +b ₁ X ₁ +b ₂ X ₂ +b ₃ X ₃ +b ₄ X ₄ +... where X ₁ =x, X ₂ =x ² , X ₃ =x ³ , X ₄ =x ⁴ ...X _n =x ⁿ In this way, the one-variable high-degree polynomial is converted into a multivariate linear function, and the least square method can be used to accurately fit the polynomial coefficients. Step 2: Take out each column of the photo separately, and repeat the operation of step 1. Step 3: In order to ensure the continuity of the fitting model as much as possible, take the mean value of the two models as the final optimal distribution model of stray light intensity in space, calculate the PSNR of the model, and find the local optimal solution by exhaustive search ξ maximizes the PSNR of the generative model. Combining the stray light of A3 with the camera exposure time and the change law of light source brightness, the stray light value of each pixel in the collected photos can be obtained, and then the influence of stray light can be removed accurately and in real time.

Wherein MSE is the mean square error of the model and the original image, n is the number of digits of the image, and the image used in the present invention is 16 bits.

To sum up, the embodiment of the present invention can effectively reduce the influence of non-imaging light (referred to as stray light) reaching the surface of the image sensor during the imaging process on the final imaging, greatly improving the clarity of the image, not only improving the effectiveness of information, but also The accuracy of subsequent image processing algorithms can be improved. The present invention is applied to a multi-dimensional, multi-scale and high-resolution computing imaging instrument (which can be understood as a huge microscope). The light source, objective lens, and camera array of the instrument are all fixed, and it can be considered that the optical path is fixed for a long time . Therefore, the spatial distribution and intensity of stray light on the surface of the image sensor is also fixed under certain conditions. Collect stray light data under different light source brightness and different camera exposure time in advance, analyze it, establish the spatial distribution model of stray light, find the relationship between stray light intensity, light source brightness and camera exposure time, and then realize the The relevant parameters of time can be used to generate the corresponding stray light spatial distribution model in real time, and the influence of stray light can be removed before image storage, which improves the experimental efficiency and effectively improves the picture clarity.

In addition, the embodiment of the present invention innovatively proposes to find out the photoelectric characteristics of the instrument in advance, which greatly reduces the time complexity of the algorithm, so that the stray light of the image captured by the high-definition microscope camera can be eliminated in real time.

According to the stray light removal method of microscopic imaging based on photoelectric characteristics prior proposed by the embodiment of the present invention, aiming at the property that the optical path of the imaging instrument does not change in spatial position within a period of time, it is collected in advance under different light source brightness and different camera exposure time Analyze the stray light data, and complete the complex modeling calculation in the usual spare time, which reduces the time complexity of the real-time algorithm, and can generate the corresponding stray light spatial distribution model in real time according to the relevant parameters when taking photos , the influence of stray light is removed before the image is stored, thereby reducing the time complexity of the algorithm, not only effectively improving the efficiency of the experiment, but also effectively improving the clarity of the image.

Next, a stray light removal device based on photoelectric property prior microscopic imaging proposed according to an embodiment of the present invention will be described with reference to the accompanying drawings.

FIG. 3 is a schematic structural diagram of a stray light removal device based on photoelectric characteristic prior microscopic imaging according to an embodiment of the present invention.

As shown in FIG. 3 , the stray light removal device 10 based on photoelectric characteristic prior microscopic imaging includes: a first photographing module 100 , a second photographing module 200 , an elimination module 300 and a processing module 400 .

Wherein, the first photographing module 100 is used for arranging a dark room and taking points at logarithmic intervals on the exposure time parameter interval supported by the camera, and taking the first photograph. The second photographing module 200 is used to turn on only the light source of the instrument in the darkroom, use light traps under the objective lens to absorb light, combine the exposure time of the camera with the brightness of the light source, uniformly take points on the brightness of the light source, and use the exposure time of the camera as in step A1 Take the same point and take a second photo. The elimination module 300 is used to subtract the first photo from the second photo to eliminate the interference of the camera's own image sensor on the experimental results, and then obtain the change law of stray light on the exposure time of the camera and the brightness of the light source. The processing module 400 is used to obtain the stray light value of each pixel in the captured photo according to the change law when analyzing the optimal distribution model of stray light intensity in space for a specific image, so as to remove the influence of stray light. The device 10 of the embodiment of the present invention generates the corresponding stray light spatial distribution model in real time, and removes the influence of stray light before image storage, thereby reducing the time complexity of the algorithm, not only effectively improving the experimental efficiency, but also effectively improving the clarity of the picture Spend.

Further, in one embodiment of the present invention, the elimination module 300 is also used to perform median filtering on the first photo and the second photo, and take the average gray value of the middle half of each picture as the photo The gray value of the gray value, respectively, in the case of only considering the exposure time of the camera and the brightness of the light source, a linear fitting is performed on a group of photos, so as to obtain the binary primary value of the average gray value with respect to the exposure time of the camera and the brightness of the light source according to the two sets of fitting coefficients function.

Further, in one embodiment of the present invention, the processing module 400 is also used to separately extract each line of the photo for piecewise linear fitting, and divide it into three segments by using a threshold method, where the threshold is the average gray level of the photo Value multiplied by the correction coefficient, the local maximum value of peak signal-to-noise ratio is obtained by exhaustive search method, polynomial fitting is performed on each segment, and each column of the photo is taken out separately, and the above operation is repeated, and the model is taken twice The mean value of the stray light intensity is used as the optimal distribution model of the final stray light intensity in space, and the stray light value is obtained by combining the change rule.

Furthermore, in one embodiment of the present invention, the polynomial of high degree in one variable is transformed into a multivariate linear function, and the least square method is used to accurately fit the coefficients of the polynomial to realize polynomial fitting.

Further, in one embodiment of the present invention, the calculation formula of peak signal-to-noise ratio is:

Among them, MSE is the mean square error between the model and the original image, and n is the number of bits of the image.

It should be noted that the foregoing explanations on the embodiment of the stray light removal method based on photoelectric characteristic prior microscopic imaging are also applicable to the stray light removal device based on photoelectric characteristic prior microscopic imaging of this embodiment, here No longer.

According to the stray light removal device for microscopic imaging based on photoelectric characteristics prior proposed by the embodiment of the present invention, aiming at the property that the optical path of the imaging instrument does not change in spatial position within a period of time, it is collected in advance under different light source brightness and different camera exposure time Analyze the stray light data, and complete the complex modeling calculation in the usual spare time, which reduces the time complexity of the real-time algorithm, and can generate the corresponding stray light spatial distribution model in real time according to the relevant parameters when taking photos , the influence of stray light is removed before the image is stored, thereby reducing the time complexity of the algorithm, not only effectively improving the efficiency of the experiment, but also effectively improving the clarity of the image.

In describing the present invention, it should be understood that the terms "center", "longitudinal", "transverse", "length", "width", "thickness", "upper", "lower", "front", " Back", "Left", "Right", "Vertical", "Horizontal", "Top", "Bottom", "Inner", "Outer", "Clockwise", "Counterclockwise", "Axial", The orientation or positional relationship indicated by "radial", "circumferential", etc. is based on the orientation or positional relationship shown in the drawings, and is only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying the referred device or element Must be in a particular orientation, be constructed in a particular orientation, and operate in a particular orientation, and therefore should not be construed as limiting the invention.

In addition, the terms "first" and "second" are used for descriptive purposes only, and cannot be interpreted as indicating or implying relative importance or implicitly specifying the quantity of indicated technical features. Thus, the features defined as "first" and "second" may explicitly or implicitly include at least one of these features. In the description of the present invention, "plurality" means at least two, such as two, three, etc., unless otherwise specifically defined.

In the present invention, unless otherwise clearly specified and limited, terms such as "installation", "connection", "connection" and "fixation" should be understood in a broad sense, for example, it can be a fixed connection or a detachable connection , or integrated; it may be mechanically connected or electrically connected; it may be directly connected or indirectly connected through an intermediary, and it may be the internal communication of two components or the interaction relationship between two components, unless otherwise specified limit. Those of ordinary skill in the art can understand the specific meanings of the above terms in the present invention according to specific situations.

In the present invention, unless otherwise clearly specified and limited, the first feature may be in direct contact with the first feature or the first and second feature may be in direct contact with the second feature through an intermediary. touch. Moreover, "above", "above" and "above" the first feature on the second feature may mean that the first feature is directly above or obliquely above the second feature, or simply means that the first feature is higher in level than the second feature. "Below", "beneath" and "beneath" the first feature may mean that the first feature is directly below or obliquely below the second feature, or simply means that the first feature is less horizontally than the second feature.

In the description of this specification, descriptions with reference to the terms "one embodiment", "some embodiments", "example", "specific examples", or "some examples" mean that specific features described in connection with the embodiment or example , structure, material or feature is included in at least one embodiment or example of the present invention. In this specification, the schematic representations of the above terms are not necessarily directed to the same embodiment or example. Furthermore, the described specific features, structures, materials or characteristics may be combined in any suitable manner in any one or more embodiments or examples. In addition, those skilled in the art can combine and combine different embodiments or examples and features of different embodiments or examples described in this specification without conflicting with each other.

Although the embodiments of the present invention have been shown and described above, it can be understood that the above embodiments are exemplary and should not be construed as limiting the present invention, and those skilled in the art can make the above-mentioned The embodiments are subject to changes, modifications, substitutions and variations.

Claims (6)

1. A stray light removal method based on photoelectric characteristic priori microscopic imaging, is characterized in that, comprises the following steps: Step A1: Arrange the darkroom and take the logarithmically intermittent points on the exposure time parameter interval supported by the camera, and take the first photo; Step A2: Turn on only the light source of the instrument in the darkroom, use a light trap to absorb light under the objective lens, combine the exposure time of the camera with the brightness of the light source, take points evenly on the brightness of the light source, and compare the exposure time of the camera with the brightness of the light source. The step A1 takes the same point position, and takes the second photo; Step A3: Subtract the first photo from the second photo to eliminate the interference of the camera's own image sensor on the experimental results, and then obtain the change law of stray light on the exposure time of the camera and the brightness of the light source, wherein , the step A3 further includes: performing median filtering on the first photo and the second photo, and taking the average gray value of the middle half of each picture as the gray value of the photo, Carry out linear fitting to a group of photos under the condition of only considering the exposure time of the camera and the brightness of the light source, so as to obtain the average gray value with respect to the exposure time of the camera and the brightness of the light source according to two sets of fitting coefficients. Binary linear functions; and Step A4: When analyzing the optimal distribution model of stray light intensity in space for a specific picture, the stray light value of each pixel in the collected photo is obtained according to the change rule, so as to remove the influence of stray light, wherein, the Said step A4, further comprising: Step 1: each row of the photo is taken out separately to carry out piecewise linear fitting, and adopts the method of threshold value to be divided into three sections, and the threshold value is the gray value of the photo multiplied by the correction coefficient, and through exhaustive search The method obtains the local maximum value of peak signal-to-noise ratio, and performs polynomial fitting on each segment; Step 2: Take out each column of the photo separately, and repeat the operation of Step 1; Step 3: Take the model twice The mean value of is used as the optimal distribution model of the final stray light intensity in space, and the stray light value is obtained in combination with the change rule. 2. the method for removing stray light based on photoelectric characteristic prior microscopic imaging according to claim 1, is characterized in that, one yuan high degree polynomial is converted into multivariate linear function, and uses least square method to accurately fit polynomial coefficient, Implements polynomial fitting. 3. The method for removing stray light based on photoelectric characteristic prior microscopic imaging according to claim 1 or 2, wherein the calculation formula of the peak signal-to-noise ratio is: Among them, MSE is the mean square error between the model and the original image, and n is the number of bits of the image. 4. A stray light removal device based on photoelectric characteristic prior microscopic imaging, characterized in that, comprising: The first shooting module is used to arrange the darkroom and take the logarithmically intermittent points on the exposure time parameter interval supported by the camera to take the first photo; The second shooting module is used to turn on only the light source of the instrument in the dark room, use a light trap to absorb light under the objective lens, combine the exposure time of the camera with the brightness of the light source, uniformly take points on the brightness of the light source, and The exposure time is the same as that of the first shooting module, and the second photo is taken; The elimination module is used to subtract the first photo from the second photo to eliminate the interference of the image sensor of the camera itself on the experimental results, and then obtain the change law of stray light on the exposure time of the camera and the brightness of the light source , wherein the elimination module is also used to perform median filtering on the first photo and the second photo, and take the average gray value of the middle half of each picture as the gray value of the photo , linearly fitting a group of photos under the condition of only considering the camera exposure time and the light source brightness respectively, so as to obtain the average gray value with respect to the camera exposure time and the light source brightness according to two sets of fitting coefficients binary linear function of ; and The processing module is used to obtain the stray light value of each pixel in the collected photo according to the change law when analyzing the optimal distribution model of stray light intensity in space for a specific picture, so as to remove the influence of stray light, wherein , the processing module is also used to separately extract each line of the photo to perform piecewise linear fitting, and divide it into three sections by using a threshold method, the threshold is the gray value of the photo multiplied by the correction coefficient, and through the method of exhaustive search Get the local maximum value of peak signal-to-noise ratio, perform polynomial fitting on each segment, and take out each column of the photo separately, and repeat the above operation, and take the mean value of the two models as the final stray light intensity in The distribution model is optimized in space, and the value of stray light is obtained in combination with the change rule. 5. The stray light removing device based on photoelectric characteristic prior microscopic imaging according to claim 4, is characterized in that, the one-dimensional high-order polynomial is converted into a multivariate linear function, and the polynomial coefficients are accurately fitted using the least squares method, Implements polynomial fitting. 6. The stray light removal device based on photoelectric characteristic prior microscopic imaging according to claim 4 or 5, wherein the calculation formula of the peak signal-to-noise ratio is: Among them, MSE is the mean square error between the model and the original image, and n is the number of bits of the image.