Image enhancement filtering method based on simple function construction under weak light condition of fluorescence microscope

Abstract

The invention discloses an image enhancement filtering method based on simple function construction under a weak light condition of a fluorescence microscope. The method comprises the following steps: acquiring an image under the weak light condition through the fluorescence microscope; the obtained image is preprocessed; constructing an image enhancement filtering module based on a csch function and a sech function; and performing image enhancement filtering processing on the preprocessed image through the constructed image enhancement filtering module, and drawing an output image according to a filtered value. According to the invention, the research focus is put on the fluorescence microscope, the application of the fluorescence microscope in the aspects of array structure image processing, Gaussian noise image processing, weak light environment image processing and the like is discussed, and the image enhancement filtering method has the advantages of simple structure and easiness in implementation, and has potential application in target identification and computer vision.

Description

Technical Field

[0001] The invention belongs to the technical field of image enhancement, and relates to image enhancement under weak light conditions of a fluorescence microscope, and specifically to an image enhancement filtering method based on a simple function under weak light conditions of a fluorescence microscope. Background Art

[0002] Image enhancement is an important part of image processing. It can improve image contrast, highlight details, and suppress noise. Most low-light images taken under low-light conditions usually look noisy and dark. This brings difficulties to subsequent tasks such as tracking, detection, and recognition in computer vision. For better understanding, high-quality clear images are required. Therefore, enhancing low-light images is particularly important.

[0003] In life sciences, fluorescence microscopy is often used to study the absorption, transport, distribution, and localization of chemicals in cells or tissues. Images obtained from a fluorescence microscope can provide information features. The enhancement of images obtained from this instrument is crucial. During the signal acquisition process, many defects may be associated with the acquired signals. In particular, when the target object is under poor lighting conditions, including weak light and unbalanced light, it will cause problems with light intensity distortion. Sometimes the target object is surrounded by dust, haze, or underwater environments, which will cause noise amplification problems in imaging. These problems require image enhancement technology to improve image quality.

[0004] Weak light images are obtained from fluorescence microscopy where the emitted fluorescence comes from the image contrast or the sample itself is too low. This may cause some or most areas of the image to be dark or underexposed. It affects medical or biological or biochemical diagnosis. Therefore, it is crucial to improve the contrast and observability of images obtained from poor fluorescence conditions.

[0005] Publication No. CN113392702A discloses a method for target recognition in a weak light environment based on adaptive image enhancement. The limitation of this method is that it relies on machine learning and computing resources, resulting in its effect on improving the contrast and observability of images obtained under fluorescent conditions is not very ideal. Summary of the invention

[0006] Purpose of the invention: In order to overcome the deficiencies in the prior art, an image enhancement filtering method based on a simple function is provided under weak light conditions of a fluorescence microscope.

[0007] Technical solution: To achieve the above purpose, the present invention provides an image enhancement filtering method based on a simple function under weak light conditions of a fluorescence microscope, comprising the following steps:

[0008] S1: Acquire images under low light conditions by fluorescence microscopy;

[0009] S2: preprocessing the image obtained in step S1;

[0010] S3: Construct image enhancement filtering module based on csch function and sech function;

[0011] S4: Perform image enhancement filtering on the preprocessed image through the constructed image enhancement filtering module, and draw the output image according to the filtered value.

[0012] Furthermore, the preprocessing in step S2 includes grayscale conversion and double-precision storage;

[0013] Grayscale conversion: Processing individual pixels to change the grayscale range of the original image data to achieve a good visual change.

[0014] Double-precision storage: For a 64-bit floating-point number, the highest bit is the sign bit S, the next 11 bits are the exponent E, and the remaining 52 bits are the valid digits M. Storage in memory is implemented in accordance with the IEEE 754 standard.

[0015] Furthermore, the image enhancement filtering module in step S3 includes an image enhancement filtering algorithm based on a csch function and an image enhancement filtering algorithm based on a sech function. The present invention is based on the combination of simple functions and universal functions, and enhances images composed of signals of different frequencies through special functions, and defines two algorithms using the hyperbolic function csch function and sech function, respectively, to form a csch filter and a sech filter, respectively.

[0016] Furthermore, the image enhancement filtering algorithm based on the csch function in step S3 includes:

[0017] A1: Read a JPG image and convert it into a grayscale image;

[0018] A2: Introduce the lifting wavelet transform method, use the gray value of the image as the input value, and obtain the transformed matrix t;

[0019] A3: Convert matrix t into matrix t7 by the following equation;

[0020] t2=a1*t+a2

[0021] t3=(a3*t2) a4

[0022] t4=sin(t3)

[0023] t6=t3 / (csch(t4+a5)

[0024] t7=a6*t6

[0025] Among them, a1, a2, a3, a4, a5, a6 are constants;

[0026] A4: Draw the output image according to the value of t7.

[0027] Furthermore, the image enhancement filtering algorithm based on the sech function in step S3 includes:

[0028] B1: Read a color image in JPG format and convert it into a grayscale image. The image size is 1064*1064;

[0029] B2: In order to obtain the transformation matrix s, the lifting wavelet transform method is introduced, and the gray value of the image is used as the input value of the lifting wavelet transform;

[0030] B3: Apply the following equation to transform the matrix s into s6;

[0031]

[0032] s4=cos(s3)

[0033] s5=s3 / sech(s4+b 10 )

[0034] s6=b 11 *s5

[0035] Among them, b1, b2, b3, b4, b5, b6, b7, b8, b9, b 10 , b 11 is a constant;

[0036] B4: Draw the output image according to the value of s6.

[0037] Furthermore, the image enhancement filtering process in step S4 includes image compression, and the image enhancement filtering algorithm based on the csch function is modified for image compression. The specific framework includes the following steps:

[0038] C1: read a color image q and divide q into three branches u1, u2, u3;

[0039] u1=q(:,:,1)

[0040] u2=q(:,:,2)

[0041] u3=q(:,:,3)

[0042] C2: Select u1 for further processing and transform the value of u1 using the following equation:

[0043] f=c1*u1

[0044] N = length(f)

[0045] T=N / 2

[0046] f1=f([1:2:N-1],[1:2:N-1])

[0047] f2=f([2:2:N],[2:2:N])

[0048] f3=c2*f1

[0049]

[0050] f6=cos(f5)

[0051] f7=c 13 *f5 / (csch(f6+c 12 ))

[0052] Among them, c1, c2, c3, c4, c5, c6, c7, c8, c9, c 10 , c 11 , c 12 , c 13 , is a constant;

[0053] C3: Draw the compressed image according to the value of f7.

[0054] Furthermore, the image enhancement filtering process in step S4 includes image compression, and the image enhancement filtering algorithm based on the sech function is modified for image compression. The specific framework includes the following steps:

[0055] D1: Read a color image Q and divide Q into the following three branches Q1, Q2, Q3

[0056] Q1=Q(:,:,1)

[0057] Q2=Q(:,:,2)

[0058] Q3=Q9:,:,3)

[0059] D2: Select Q1 for further processing and transform the value of Q1 using the following equation

[0060] k=d1*Q1

[0061] NN=length(k)

[0062] TT=NN / 2

[0063] k1=k([1:2:N-1],[1:2:N-1])

[0064] k2=k([2:2:N],[2:2:N])

[0065]

[0066]

[0067] k5=cos(k4)

[0068] k6=d 12 *k4 / (sech(k5+d 11 ))

[0069] Among them, d1, d2, d3, d4, d5, d6, d7, d8, d9, d 10 , d 11 , d 12 , d 13 , is a constant;

[0070] D3: Draw the compressed image according to the value of k6.

[0071] Furthermore, the following formula is used to calculate the compression ratio in the image compression:

[0072] CR=(VoL1-VoL2) / VoL1

[0073] Among them, VoL1 is the volume of the input image, and VoL2 is the volume of the compressed output image.

[0074] Furthermore, the image enhancement filtering process in step S4 includes color processing the filtered black and white image, including the following steps:

[0075] E1: define a constant nbc, and use the value of nbc and the matrix u obtained by processing with the csch filter or sech filter to introduce the coding;

[0076] E2: Introduce three vectors, expressed as:

[0077] thr_h =

[1619]

[0078] thr_d =

[2021]

[0079] thr_v =

[2223]

[0080] E3: Create a matrix thr, expressed as:

[0081] thr = [thr_h; thr_h; thr_h]

[0082] E4: Based on the 'dbN' wavelet transform, the hard threshold function, and the value of the matrix thr, a compression method based on wavelet transform is generated, which can generate coefficient value u2, u2 is encoded by nbc to obtain u3, and the value of u3 is used to draw a color image.

[0083] Beneficial effects: Compared with the prior art, the present invention has the following advantages:

[0084] 1. Compared with the traditional image enhancement that relies on machine learning methods, the present invention surpasses the wavelet transform method and can complete image enhancement by combining simple functions. The increase of scale function in wavelet transform will lead to a situation where the spatial resolution becomes higher and the frequency resolution becomes lower. The mathematical function introduced in the framework provided by the present invention has the excellent performance of further segmenting and narrowing the spectral window that becomes wider with the increase of scale function, thereby overcoming the shortcomings of wavelet transform, and the function can be flexibly modified and can be used to process various images after modification.

[0085] 2. The two filters based on special functions designed by the present invention are suitable for processing a series of images with structures such as fluorescence microscope images, weak light images, and Gaussian noise highly degraded images. In addition, they can also be developed into a framework for image edge detection, compression and coloring. It can be seen that the compression ratio can reach 0.9688. Different coloring effects can be produced by setting different thresholds.

[0086] 3. The present invention focuses on fluorescence microscopy and explores its application in array structure image processing, Gaussian noise image processing, low light environment image processing, etc. In view of the advantages of simple structure and easy implementation, the image enhancement filtering method of the present invention has potential uses in target recognition and computer vision. BRIEF DESCRIPTION OF THE DRAWINGS

[0087] Figure 1 It is a schematic diagram of the process of the present invention;

[0088] Figure 2 A comparison diagram of an image obtained from a fluorescence microscope provided in Example 1;

[0089] Figure 3 A comparison diagram of images processed under low light conditions provided in Example 2;

[0090] Figure 4 A comparison diagram of the process provided in Example 3 for enhancing a highly degraded image using Gaussian noise;

[0091] Figure 5 A comparison chart of the application effect of image edge detection provided in Example 4;

[0092] Figure 6A comparison chart of the effects of image compression application provided in Example 5;

[0093] Figure 7 This is a comparison chart of the effects of image colorization application provided in Example 6. DETAILED DESCRIPTION

[0094] The present invention is further explained below in conjunction with the accompanying drawings and specific embodiments. It should be understood that these embodiments are only used to illustrate the present invention and are not used to limit the scope of the present invention. After reading the present invention, various equivalent forms of modifications to the present invention by those skilled in the art all fall within the scope defined by the claims attached to this application.

[0095] like Figure 1 As shown, the present invention provides an image enhancement filtering method based on a simple function under weak light conditions of a fluorescence microscope, comprising the following steps:

[0096] S1: Acquire images under low light conditions by fluorescence microscopy;

[0097] Images acquired using fluorescence microscopy are used to study the absorption, transport, distribution, and localization of chemicals in cells or tissues.

[0098] Among them, weak light conditions refer to light intensity distortion caused by weak light and unbalanced light, and sometimes include dust, haze or underwater environment around the target object.

[0099] S2: Preprocess the image obtained in step S1, including grayscale conversion and double-precision storage:

[0100] Grayscale conversion: Processing is performed on independent pixels, and the image is visually improved by changing the grayscale range occupied by the original image data; the purpose of grayscale conversion is to improve the image quality and make the image display clearer.

[0101] Double-precision storage: For a 64-bit floating point number, the highest bit is the sign bit S, the next 11 bits are the exponent E, and the remaining 52 bits are the valid digits M. The storage in memory is implemented according to the IEEE 754 standard. In general, images are stored as uint8 data. When performing data operations on images, overflow occurs when the data is greater than 256, and the result is distorted. However, overflow will not occur when double-precision numbers are operated. Therefore, converting uint8 data to double-precision numbers first will not distort the calculation accuracy.

[0102] S3: Construct image enhancement filtering module based on csch function and sech function;

[0103] The image enhancement filtering module includes an image enhancement filtering algorithm based on a Csch function and an image enhancement filtering algorithm based on a Sech function. The present invention is based on the combination of simple functions and universal functions, and enhances images composed of signals of different frequencies through special functions. Two algorithms are defined using the hyperbolic function Csch function and Sech function, respectively, to form a Csch filter and a Sech filter. In the present invention, the Csch filter and the Sech filter exist in parallel and operate independently, and can be selected according to specific conditions and requirements.

[0104] Image enhancement filtering algorithms based on the csch function include:

[0105] A1: Read a JPG image and convert it into a grayscale image;

[0106] A2: Introduce the lifting wavelet transform method, use the gray value of the image as the input value, and obtain the transformed matrix t;

[0107] A3: Convert matrix t into matrix t7 by the following equation;

[0108] t2=a1*t+a2

[0109] t3=(a3*t2) a4

[0110] t4=sin(t3)

[0111] t6=t3 / (csch(t4+a5)

[0112] t7=a6*t6

[0113] Among them, a1, a2, a3, a4, a5, a6 are constants;

[0114] A4: Draw the output image according to the value of t7.

[0115] Image enhancement filtering algorithms based on sech functions include:

[0116] B1: Read a color image in JPG format and convert it into a grayscale image. The image size is 1064*1064;

[0117] B2: In order to obtain the transformation matrix s, the lifting wavelet transform method is introduced, and the gray value of the image is used as the input value of the lifting wavelet transform;

[0118] B3: Apply the following equation to transform the matrix s into s6;

[0119]

[0120] s4=cos(s3)

[0121] s5=s3 / sech(s4+b 10 )

[0122] s6=b 11 *s5

[0123] Among them, b1, b2, b3, b4, b5, b6, b7, b8, b9, b 10 , b 11 is a constant;

[0124] B4: Draw the output image according to the value of s6.

[0125] S4: Perform image enhancement filtering on the preprocessed image through the constructed image enhancement filtering module, and draw the output image according to the filtered value.

[0126] The values ​​of these constants in steps A3 and B3 of the present invention are adjusted accordingly according to the output effect (contrast, brightness, etc.) of the final image. Different images have different values. For example, the value of one of the images is as follows:

[0127] t3=t2; a1=52; a2=1; a3=1; t4=a2*t3+a3; t5=(1*t4).^146; t6=sin(t5); a5=0.2;

[0128] t7=t5. / (csch(t6+a5));figure(2);q4=200000000000000000000*t7;

[0129] Image enhancement filtering includes image compression. There are two ways to compress images, as follows:

[0130] Method 1: Modify the image enhancement filtering algorithm based on the csch function for image compression. The specific framework includes the following steps:

[0131] C1: read a color image q and divide q into three branches u1, u2, u3;

[0132] u1=q(:,:,1

[0133] u2=q(:,:,2

[0134] u3=q(:,:,3

[0135] C2: Select u1 for further processing and transform the value of u1 using the following equation:

[0136] f=c1*u1

[0137] N = length(f)

[0138] T=N / 2

[0139] f1=f([1:2:N-1],[1:2:N-1])

[0140] f2=f([2:2:N],[2:2:N])

[0141] f3=c2*f1

[0142]

[0143] f6=cos(f5)

[0144] f7=c 13 *f5 / (csch(f6+c 12 ))

[0145] Among them, c1, c2, c3, c4, c5, c6, c7, c8, c9, c 10 , c 11 , c 12 , c 13 , is a constant;

[0146] C3: Draw the compressed image according to the value of f7.

[0147] Method 2: Modify the image enhancement filtering algorithm based on the sech function for image compression. The specific framework includes the following steps:

[0148] D1: Read a color image Q and divide Q into the following three branches Q1, Q2, Q3

[0149] Q1=Q(:,:,1

[0150] Q2=Q(:,:,2

[0151] Q3=Q(:,:,3

[0152] D2: Select Q1 for further processing and transform the value of Q1 using the following equation

[0153] k=d1*Q1

[0154] NN=length(k)

[0155] TT=NN / 2

[0156] k1=k([1:2:N-1],[1:2:N-1])

[0157] k2=k([2:2:N],[2:2:N])

[0158]

[0159] k5=cos(k4)

[0160] k6=d 12 *k4 / (sech(k5+d 11 ))

[0161] Among them, d1, d2, d3, d4, d5, d6, d7, d8, d9, d 10 , d 11 , d 12 , d 13 , is a constant;

[0162] D3: Draw the compressed image according to the value of k6.

[0163] The following formula is used to calculate the compression ratio in image compression:

[0164] CR=(VoL1-VoL2) / VoL1

[0165] Among them, VoL1 is the volume of the input image, and VoL2 is the volume of the compressed output image.

[0166] The image enhancement filtering process also includes color processing of the filtered black and white image, including the following steps:

[0167] E1: define a constant nbc, and use the value of nbc and the matrix u obtained by processing with the csch filter or sech filter to introduce the coding;

[0168] E2: Introduce three vectors, expressed as:

[0169] thr_h =

[1619]

[0170] thr_d =

[2021]

[0171] thr_v =

[2223]

[0172] E3: Create a matrix thr, expressed as:

[0173] thr = [thr_h; thr_h; thr_h]

[0174] E4: Based on the 'dbN' wavelet transform, the hard threshold function, and the value of the matrix thr, a compression method based on wavelet transform is generated, which can generate coefficient value u2, u2 is encoded by nbc to obtain u3, and the value of u3 is used to draw a color image.

[0175] Based on the above content, in order to verify the effectiveness and effect of the method of the present invention, the method of the present invention is applied in examples, specifically the following six embodiments, in which entropy, signal-to-noise ratio (SNR), peak signal-to-noise ratio (PSNR) and normalized mean square error (NMSE) are used to evaluate the quality of the processed image, as follows:

[0176] Example 1: Figure 2 As shown, Figure 2 (a) is a green fungus image obtained from a fluorescence microscope in this example, (b) is the image processed by the CSCHI filter, and (c) is the image processed by the SECH filter. The image quality after the two filters are processed is shown in Table 1.

[0177] Table 1 Evaluation values ​​of green fungus images

[0178]

[0179]

[0180] Among them, entropy: the larger the better; signal-to-noise ratio (SNR): the larger the better; peak signal-to-noise ratio (PSNR): the larger the better; normalized mean square error (NMSE): the smaller the better. From Table 1, we can see that the csch filter and sech filter have their own advantages and disadvantages when comparing the effects of the two algorithms for different types of images.

[0181] Example 2: Figure 3 As shown, Figure 3 (a) is a tree image obtained from a fluorescence microscope under weak light conditions in this example, (b) is the image processed by the CSCHI filter, and (c) is the image processed by the SECH filter. The image quality after the two filters are processed is shown in Table 2.

[0182] Table 2 Evaluation values ​​of tree images

[0183] Filters Entropy NMSE SNR PSNR csch 0.0177 0.1914 7.1816 43.7168 sech 2.1549 0.1380 8.6016 45.1368

[0184] It can also be seen from Table 2 that the two algorithms are compared for different types of images. The csch filter and the sech filter have their own advantages and disadvantages.

[0185] Example 3: Considering that filters are effective for enhancing the image contrast of images obtained from low-light illumination, we assume that they may be useful for enhancing the image contrast of noise-degraded images, using an image of cherry blossoms for demonstration. Figure 4 As shown, Figure 4(a) is a cherry blossom image, (b) is a Gaussian noise with a mean of 1, (c) is the image processed by the csch filter, and (d) is the image processed by the sech filter. The image quality after the two filters are shown in Table 3.

[0186] Table 3 Evaluation values ​​of cherry blossom images

[0187] Filters Entropy csch 0.0615 sech 1.3543

[0188] It can be seen from Table 3 that the sech filter has a better processing effect on the image.

[0189] Example 4: For edge detection, two filters are explored for extraction Figure 5 The application of the configuration file in the array object shown in a has the following structures: Figure 5 As shown in b and c, it can be found that the two filters are effective for detecting the contours of array objects.

[0190] Example 5: For image compression, use the compression algorithms of method 1 and method 2 to compress a building image. Figure 6 shown.

[0191] Figure 6 (a) is the Building input image used for compression, (b) is the image compression result using the first algorithm, and (c) is the image compression result using the second algorithm.

[0192] Among them, the compression ratio (CR) method is:

[0193] CR=(VoL1-VoL2) / VoL1

[0194] Among them, VoL1 is the volume of the input image, and VoL2 is the volume of the compressed output image. The first and second algorithms are used to Figure 6 The compression ratio of a is 0.9688.

[0195] Example 6: Figure 7 As shown in (a), a gray image named Wall is colored by combining a csch filter or a sech filter. The structure is as follows Figure 7 As shown in (b) and (c), in order to show different coloring effects using different thr_h values, the grayscale image obtained from the filter can be converted into a color image.

Claims

1. An image enhancement filtering method based on a simple function under weak light conditions of a fluorescence microscope, characterized in that: The steps include: S1: Acquire images under low light conditions by fluorescence microscopy; S2: preprocessing the image obtained in step S1; S3: Construct image enhancement filtering module based on csch function and sech function; S4: Perform image enhancement filtering on the preprocessed image through the constructed image enhancement filtering module, and draw the output image according to the filtered value.

2. The image enhancement filtering method based on simple functions under weak light conditions of fluorescence microscope according to claim 1, characterized in that: The preprocessing in step S2 includes grayscale conversion and double-precision storage.

3. The image enhancement filtering method based on simple function under weak light conditions of fluorescence microscope according to claim 1, characterized in that: The image enhancement filtering module in step S3 includes an image enhancement filtering algorithm based on a csch function and an image enhancement filtering algorithm based on a sech function.

4. The image enhancement filtering method based on simple functions under weak light conditions of fluorescence microscope according to claim 3, characterized in that: The image enhancement filtering algorithm based on the csch function in step S3 includes: A1: Read a JPG image and convert it into a grayscale image; A2: Introduce the lifting wavelet transform method, use the gray value of the image as the input value, and obtain the transformed matrix t; A3: Convert matrix t into matrix t7 by the following equation; t2=a1*t+a2 t4=sin(t3) t6=t3 / (csch(t4+a5) t7=a6*t6 Among them, a1, a2, a3, a4, a5, a6 are constants; A4: Draw the output image according to the value of t7.

5. The image enhancement filtering method based on simple functions under weak light conditions of fluorescence microscope according to claim 3, characterized in that: The image enhancement filtering algorithm based on the sech function in step S3 includes: B1: Read a color image in JPG format and convert it into a grayscale image; B2: In order to obtain the transformation matrix s, the lifting wavelet transform method is introduced, and the gray value of the image is used as the input value of the lifting wavelet transform; B3: Apply the following equation to transform the matrix s into s6; s4=cos(s3) s5=s3 / s(s4+b 10 ) s6=b 11 *s5 Among them, b1, b2, b3, b4, b5, b6, b7, b8, b9, b 10 , b 11 is a constant; B4: Draw the output image according to the value of s6.

6. The image enhancement filtering method based on simple functions under weak light conditions of fluorescence microscope according to claim 4, characterized in that: The image enhancement filtering process in step S4 includes image compression, and the image enhancement filtering algorithm based on the csch function is modified for image compression. The specific framework includes the following steps: C1: read a color image q and divide q into three branches u1, u2, u3; u1=q(:,:,1) u2=q(:,:,2) u3=q(:,:,3) C2: Select u1 for further processing and transform the value of u1 using the following equation: f=c1*u1 N = length(f) T=N / 2 f1=f([1:2:N-1],[1:2:N-1]) f2=f([2:2:N],[2:2:N]) f3=c2*f1 f6=cos(f5) f7=c 13 *f5 / (csch(f6+c 12 )) Among them, c1, c2, c3, c4, c5, c6, c7, c8, c9, c 10 , c 11 , c 12 , c 13 , is a constant; C3: Draw the compressed image according to the value of f7.

7. The image enhancement filtering method based on simple functions under weak light conditions of fluorescence microscope according to claim 5, characterized in that: The image enhancement filtering process in step S4 includes image compression, and the image enhancement filtering algorithm based on the sech function is modified for image compression. The specific framework includes the following steps: D1: Read a color image Q and divide Q into the following three branches Q1, Q2, Q3 Q1=Q(:,:,1) Q2=Q(:,:,2) Q3=Q(:,:,3) D2: Select Q1 for further processing and transform the value of Q1 using the following equation k=d1*Q1 NN=length(k) TT=NN / 2 k1=k([1:2:N-1],[1:2:N-1]) k2=k([2:2:N],[2:2:N]) k5=cos(k4) <h2 style=";text-align:left;direction:ltr">k6=d<h2 style=";text-align:left;direction:ltr"> 12 <h2 style=";text-align:left;direction:ltr"> *k4 / (sech(k5+d<h2 style=";text-align:left;direction:ltr"> 11 <h2 style=";text-align:left;direction:ltr"> )) Among them, d1, d2, d3, d4, d5, d6, d7, d8, d9, d 10 , d 11 , d 12 , d 13 , is a constant; D3: Draw the compressed image according to the value of k6.

8. The image enhancement filtering method based on simple functions under weak light conditions of a fluorescence microscope according to claim 6 or 7, characterized in that: The image compression ratio is calculated using the following formula: CR=(VoL1-VoL2) / VoL1 Among them, VoL1 is the volume of the input image, and VoL2 is the volume of the compressed output image.

9. The image enhancement filtering method based on simple functions under weak light conditions of fluorescence microscope according to claim 1, characterized in that: The image enhancement filtering process in step S4 includes color processing the filtered black and white image, including the following steps: E1: define a constant nbc, and use the value of nbc and the matrix u obtained by processing with the csch filter or sech filter to introduce the coding; E2: Introduce three vectors, expressed as: thr_h = [1619] thr_d = [2021] thr_v = [2223] E3: Create a matrix thr, expressed as: thr = [thr_h; thr_h; thr_h] E4: Based on the 'dbN' wavelet transform, the hard threshold function, and the value of the matrix thr, a compression method based on wavelet transform is generated, which can generate coefficient value u2, u2 is encoded by nbc to obtain u3, and the value of u3 is used to draw a color image.

Images