An analysis and detection method and device based on multi-exposure and multi-brightness dual parameters

Abstract

The present invention discloses a multi-exposure, multi-brightness, dual-parameter analysis and detection method and device, belonging to the field of immunochromatographic analysis and detection. The analysis and detection method includes obtaining a multi-exposure, multi-brightness image set under multiple multi-exposure time sequences and different light intensities. In the process of restoring the camera response function of the image acquisition system, an HDR algorithm based on multi-exposure and multi-brightness fusion is used. The weights of images with different exposures and different current values ​​are adjusted by adopting a segmented weight function. By fine-tuning the parameters of the segmented weight function to more accurately reflect the characteristics of each concentration segment in the immunochromatography, an optimal weight function is established to enhance the accuracy of the dual-parameter camera response function fitting, thereby restoring the highest-quality immunochromatographic HDR image and improving the linear range of quantitative detection.

Description

Technical Field

[0001] The present invention belongs to the field of immunochromatographic analysis and detection, and more specifically, relates to an analysis and detection method and device based on multi-exposure and multi-brightness dual parameters. Background Art

[0002] As an easy-to-operate on-site rapid detection technology, the immunochromatographic method has been widely used in many fields. The immunoassay device based on the image imaging principle can quickly, conveniently and inexpensively capture all the image information of the entire test card, providing a basis for subsequent data processing and global analysis. However, traditional HDR (high dynamic range) technology usually takes a set of photos with different exposure times under fixed light conditions, while immunochromatographic detection requires flexible adjustment of different parameters (such as light source brightness and exposure time) in the experiment according to the needs of the test object, thereby generating a series of images under different brightness conditions. This helps to more effectively restore the most realistic immunochromatographic HDR images in immunochromatographic detection, surpassing the images obtained under traditional HDR technology. This is also what traditional HDR technology has failed to address.

[0003] The immunochromatographic image sensor is essentially a specialized camera whose basic operating principle is the same as that of the digital cameras used in daily life. Therefore, the photo enhancement technology ideas of digital cameras can be borrowed to solve the dynamic range expansion problem of the immunochromatographic detection system. However, because the fluorescence signal is extremely sensitive to changes in ambient light, the HDR algorithm that fuses images of different brightness needs to precisely adjust the current to change the brightness and wait for the current to stabilize before accurate recording and shooting. In contrast, the traditional multi-exposure fusion HDR algorithm can quickly and conveniently collect the required image set by simply adjusting the exposure time parameters in the shooting software, which is suitable for dynamically changing experimental conditions.

[0004] In real-world applications, images typically obtained contain information of varying brightness and exposure levels. Traditional HDR technology has a limited dynamic range, and the camera response function that focuses solely on exposure time or light intensity cannot accurately detect the results of immunochromatographic images. This results in immunochromatographic detectors with narrow detection ranges, slow detection speeds, and low detection accuracy. Summary of the Invention

[0005] In response to the defects of related technologies, the purpose of the present invention is to provide an analysis and detection method based on multi-exposure and multi-brightness dual parameters, aiming to solve the problems of narrow detection range, slow detection speed and low detection accuracy of immunochromatographic detectors for image information with different brightness and exposure levels.

[0006] To achieve the above objectives, in a first aspect, the present invention provides an analysis and detection method based on multi-exposure and multi-brightness dual parameters, comprising:

[0007] The paper card to be tested is photographed in a multi-exposure time sequence and at different light intensities to obtain a multi-exposure and multi-brightness image set of the paper card to be tested;

[0008] According to the standard concentration quantitative curve, the multi-exposure and multi-brightness image set of the paper card to be tested, the corresponding exposure time and the corresponding current value, the actual concentration value of the paper card to be tested is obtained;

[0009] The method for generating the standard concentration quantitative curve includes:

[0010] S1. Capture X images of standard immunochromatographic test strips with different concentrations in P multi-exposure time sequences and L light intensities to obtain a multi-exposure, multi-brightness image set; divide the multi-exposure, multi-brightness image set into a first multi-exposure, multi-brightness image set including X1 images of the standard immunochromatographic test strips and a second multi-exposure, multi-brightness image set including X2 images of the standard immunochromatographic test strips; wherein P ≥ 3, L ≥ 3, and X = X1 + X2;

[0011] S2. Obtain grayscale value changes of N pixels in the T-line region of each image in the first multi-exposure multi-brightness image set to obtain a grayscale value matrix of N×P×L pixels;

[0012] S3, constructing an initial two-parameter camera response function based on the grayscale value matrix, exposure time and current value;

[0013] S4. Construct the following piecewise weight function:

[0014]

[0015] Wherein, the function forms of f1(z) and f2(z) include linear function, quadratic polynomial function, exponential function or logarithmic function, and z1 and z2 are segmentation points;

[0016] S5. Determine the function forms of f1(z) and f2(z) and the segmentation points z1 and z2 that minimize the error between the average pixel value of the T-line region and the true pixel value, and obtain a target segmentation weight function; the average pixel value of the T-line region is calculated by: optimizing the initial two-parameter camera response function using the segmentation weight function; acquiring an HDR image according to the optimized camera response function, the second multi-exposure multi-brightness image set, the corresponding exposure time, and the corresponding current value; and performing tone mapping on the HDR image to obtain the average pixel value of the T-line region;

[0017] S6. Optimizing the initial two-parameter camera response function using the target segmented weight function to obtain a target two-parameter camera response function;

[0018] S7. According to the target dual-parameter camera response function, the multi-exposure and multi-brightness image set of the standard immunochromatographic test paper card, the corresponding exposure time and the corresponding current value, the average pixel value of the T-line area of ​​the standard immunochromatographic test paper card is obtained, and the average pixel value of the T-line area of ​​the standard immunochromatographic test paper card is fitted with the corresponding concentration to generate a standard concentration quantitative curve.

[0019] Optionally, the standard immunochromatographic test paper cards have different T line concentrations and the same C line concentrations.

[0020] Optionally, S2 specifically includes:

[0021] S21, extracting a T-line region of each image in the first multi-exposure multi-brightness image set using an image segmentation algorithm;

[0022] S22, vertically stitching the T-line regions of each image in order of concentration to obtain a guide image;

[0023] S23 , obtaining grayscale value changes of N / X1 pixels in each T-line area in the guide image to obtain a grayscale value matrix of N×P×L pixels.

[0024] Optionally, the error between the average pixel value and the true pixel value is the root mean square error (RMSE) and the mean absolute percentage error (MAPE) between the average pixel value of the T-line area and the true pixel value of the standard immunochromatographic test paper card.

[0025] Optionally, S7 specifically includes:

[0026] S71, obtaining an average pixel value of the T-line region of the standard immunochromatographic test strip card according to the target dual-parameter camera response function, the multi-exposure and multi-brightness image set of the standard immunochromatographic test strip card, the corresponding exposure time, and the corresponding current value;

[0027] S72. With the average pixel value in S71 as the y-axis and the actual concentration of the standard immunochromatographic test paper card as the x-axis, a four-parameter logistic fit is used to obtain a standard concentration quantitative curve.

[0028] Optionally, the standard immunochromatographic test paper card includes a colloidal gold immunochromatographic card and a fluorescent immunochromatographic card.

[0029] Optionally, the multi-exposure and multi-brightness image set covers pixel values ​​of 0-255 at different exposure time sequences and light intensities.

[0030] Optionally, after obtaining the multi-exposure multi-brightness image set, the method further includes: pre-processing the multi-exposure multi-brightness image set using a median filtering algorithm, a Gaussian filtering algorithm, a bilateral filtering algorithm or a guided filtering algorithm.

[0031] In a second aspect, the present invention further provides an analysis and detection device based on multi-exposure and multi-brightness dual parameters, which is used to perform the analysis and detection method as described in any one of the first aspects, including:

[0032] An image acquisition module is used to photograph the paper card to be tested under multiple exposure time sequences and different light intensities to obtain a set of multi-exposure and multi-brightness images of the paper card to be tested;

[0033] A concentration analysis and detection module is used to obtain the actual concentration value of the paper card to be tested based on the standard concentration quantitative curve, the multi-exposure and multi-brightness image set of the paper card to be tested, the corresponding exposure time and the corresponding current value;

[0034] The method for generating the standard concentration quantitative curve includes:

[0035] Taking images of X standard immunochromatographic test strips of different concentrations at P multi-exposure time sequences and L light intensities to obtain a multi-exposure, multi-brightness image set; dividing the multi-exposure, multi-brightness image set into a first multi-exposure, multi-brightness image set including X1 images of the standard immunochromatographic test strips and a second multi-exposure, multi-brightness image set including X2 images of the standard immunochromatographic test strips; wherein P ≥ 3, L ≥ 3, and X = X1 + X2;

[0036] Obtaining grayscale value changes of N pixels in the T-line region of each image in the first multi-exposure multi-brightness image set to obtain a grayscale value matrix of N×P×L pixels;

[0037] constructing an initial two-parameter camera response function based on the grayscale value matrix, exposure time and current value;

[0038] Construct the following piecewise weight function:

[0039]

[0040] Wherein, the function forms of f1(z) and f2(z) include linear function, quadratic polynomial function, exponential function or logarithmic function, and z1 and z2 are segmentation points;

[0041] Determining the function forms of f1(z) and f2(z) and the segmentation points z1 and z2 that minimize the error between the average pixel value of the T-line region and the true pixel value, and obtaining a target segmentation weight function; calculating the average pixel value of the T-line region includes: optimizing the initial two-parameter camera response function using the segmentation weight function; acquiring an HDR image based on the optimized camera response function, a second multi-exposure multi-brightness image set, corresponding exposure time, and corresponding current value; performing tone mapping on the HDR image, and obtaining the average pixel value of the T-line region;

[0042] The target segmented weight function is used to optimize the initial two-parameter camera response function to obtain the target two-parameter camera response function;

[0043] According to the target dual-parameter camera response function, the multi-exposure and multi-brightness image set of the standard immunochromatographic test strip card, the corresponding exposure time and the corresponding current value, the average pixel value of the T-line area of ​​the standard immunochromatographic test strip card is obtained. The average pixel value of the T-line area of ​​the standard immunochromatographic test strip card is fitted with the corresponding concentration to generate a standard concentration quantitative curve.

[0044] Compared with the prior art, the above technical solutions conceived by the present invention can achieve the following beneficial effects:

[0045] 1. The present invention provides a dual-parameter analysis and detection method based on multiple exposures and multiple brightnesses. This method addresses the specific needs of quantitative processing for immunochromatographic images with varying brightness and exposure levels, and expands the acquisition technology for high dynamic range images based on a dual-parameter adjustment method combining limited current values ​​and multiple exposure time values. The method first restores the camera response function of the image acquisition system, then combines the camera response function curve with the biological response curve, thereby expanding the linear range of the immunochromatographic standard quantitative curve. This addresses the limitation of the currently narrow detection range of immunochromatographic detectors based on the imaging principle. By limiting the current value and exposure time, multiple camera response function curves at multiple light intensities are sequentially obtained. Image information from different brightness and exposure levels is merged to form a comprehensive dynamic range. A piecewise weight function is used to adjust the weights of the different parameter images, mitigating the impact of overexposed and underexposed areas in the image on the accuracy of the camera response function fitting. This ensures that the fitted standard quantitative curve conforms to the characteristics of the immunochromatographic S-shaped curve, significantly improving the accuracy of HDR image synthesis and making the analysis and detection results based on the immunochromatographic test strip more accurate.

[0046] 2. The present invention provides a dual-parameter analysis and detection method based on multiple exposures and multiple brightnesses. After generating a standard concentration quantitative curve, a set of multi-exposure, multi-brightness images of the test card is captured under multiple exposure and multiple light intensity parameters. This produces a highly accurate HDR image and an accurate average pixel value of the T-line region of the test card. The actual concentration value of the test card can be obtained by referring to the standard concentration quantitative curve. This method can be applied to both colloidal gold immunochromatographic test strips and fluorescent immunochromatographic test strips, and can produce accurate concentration detection results. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] Figure 1 This is a flow chart of an analysis and detection method based on multi-exposure and multi-brightness dual parameters provided by an embodiment of the present invention;

[0048] Figure 2 This is an example of a standard immunochromatographic test strip card;

[0049] Figure 3 36 standard colloidal gold immunochromatographic test strips;

[0050] Figure 4 This is a set of images of a 30# colloidal gold immunochromatographic test strip at 5 currents and 3 exposures;

[0051] Figure 5 (A) in the middle shows images of 8 immunochromatographic test strips. Figure 5 Middle (B) is a guide image formed by combining the T-line areas of 8 immunochromatographic test strip images. Figure 5 Middle (C) is to select Q pixels in each ROI area;

[0052] Figure 6 The results of the 5# test paper card obtained under four filtering algorithms, among which (A) is the original image of the 5# test paper card, (B) is the result of bilateral filtering, (C) is the result of median filtering, (D) is the result of Gaussian filtering, and (E) is the result of guided filtering;

[0053] Figure 7 This is the flow chart of the sliding window threshold segmentation algorithm;

[0054] Figure 8 (A) is the sliding window threshold segmentation algorithm to extract the C line and T line of the immunochromatographic test paper card. Figure 8 (B) is the sliding window of the immunochromatography image ROI image, selecting the area with the minimum gray value. Figure 8 Middle (C) shows the C-line and T-line regions after sliding window segmentation;

[0055] Figure 9 (A) is the three-dimensional two-parameter response function curve fitted under 5 currents and 3 exposure times. Figure 9 Middle (B) is a set of multi-brightness camera response function curves under different exposure times. Figure 9 Middle (C) is a set of multi-exposure camera response function curves under different currents;

[0056] Figure 10 (A) is the exposure offset under the identity function, Figure 10 (B) is the exposure offset under the hat function. Figure 10 Middle (C) is the exposure offset under the Gaussian function;

[0057] Figure 11 The camera response function results are fitted under different weight functions; among them, (A) is the identity function, (B) is the hat function, (C) is the Gaussian function, parameters a = 2, b = 4, (D) is the CRF curve fitted under the identity function, (E) is the CRF curve fitted under the hat function, and (F) is the CRF curve fitted under the Gaussian function;

[0058] Figure 12 (A) is the MAPE of different (a, b) parameter combinations, Figure 12 (B) RMSE of different (a, b) parameter combinations;

[0059] Figure 13 is the image of the Gaussian function when the parameters a=10, b=4;

[0060] Figure 14 Middle (A) is the quantitative curve of 36 test strips divided into 9 groups and spliced ​​at 9 exposure times. Figure 14 Middle (B) is the slope of the mapping between the T-line pixel value and the actual printed grayscale value of the 9 groups of test strips;

[0061] Figure 15 is the two-dimensional dual-parameter camera response function curve based on the optimal weight function fitting;

[0062] Figure 16 This is a collection of images and synthesized HDR images of a 30# colloidal gold immunochromatographic test strip at 5 currents and 3 exposure times;

[0063] Figure 17 This is the standard concentration quantitative curve of colloidal gold immunochromatography before and after the dual-parameter HDR algorithm;

[0064] Figure 18 This is an example of a fluorescent immunochromatographic test strip card;

[0065] Figure 19 This is a set of images of the B4 fluorescent immunochromatographic test strip at 5 currents and 3 exposure times;

[0066] Figure 20 It is a single-channel image after extracting the red component and performing guided filtering;

[0067] Figure 21 (A) The three-dimensional two-parameter response function curve fitted by the standard fluorescence color scale test paper card. Figure 21 Middle (B) is a set of multi-exposure camera response function curves under different currents. Figure 21 Middle (C) is a set of multi-brightness camera response function curves under different exposure times;

[0068] Figure 22 The two-dimensional two-parameter response function curve fitted to the standard fluorescence color scale test paper card;

[0069] Figure 23 This is the image set and synthesized HDR image of the B4 fluorescent immunochromatographic test strip card at 5 currents and 3 exposure times;

[0070] Figure 24The fitting curve results of the fluorescent immunochromatographic test strip card before and after using the dual-parameter HDR algorithm; among them, (A) is the comparison of the standard concentration quantitative curve before and after the dual-parameter HDR algorithm, and (B) is the comparison of the detection linear range in the standard concentration quantitative curve. DETAILED DESCRIPTION

[0071] In order to make the objectives, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely for the purpose of explaining the present invention and are not intended to limit the present invention. In addition, the technical features involved in the various embodiments of the present invention described below may be combined with each other as long as they do not conflict with each other.

[0072] The contents involved in the above embodiment are described below in conjunction with a preferred embodiment.

[0073] The immunochromatographic image sensor in the image acquisition system is essentially a specialized camera, operating on the same basic principle as the digital cameras used in everyday life. Therefore, the same photo enhancement techniques used in digital cameras can be leveraged to address the dynamic range expansion issue in immunochromatographic detection systems. During immunochromatographic testing, flexible adjustments to various parameters, such as light source brightness and exposure time, are required based on the needs of the test subject. This generates a series of images under varying brightness conditions, helping to more effectively restore the most realistic immunochromatographic HDR images during immunochromatographic testing, surpassing those obtained using traditional HDR techniques.

[0074] An analysis and detection method based on multi-exposure and multi-brightness dual parameters, comprising:

[0075] The paper card to be tested is photographed in a multi-exposure time sequence and at different light intensities to obtain a multi-exposure and multi-brightness image set of the paper card to be tested;

[0076] According to the standard concentration quantitative curve, the multi-exposure and multi-brightness image set of the paper card to be tested, the corresponding exposure time and the corresponding current value, the actual concentration value of the paper card to be tested is obtained;

[0077] The method for generating the standard concentration quantitative curve includes:

[0078] S1. Capture X images of standard immunochromatographic test strips with different concentrations in P multi-exposure time sequences and L light intensities to obtain a multi-exposure, multi-brightness image set; divide the multi-exposure, multi-brightness image set into a first multi-exposure, multi-brightness image set including X1 images of the standard immunochromatographic test strips and a second multi-exposure, multi-brightness image set including X2 images of the standard immunochromatographic test strips; wherein P ≥ 3, L ≥ 3, and X = X1 + X2;

[0079] S2. Obtain grayscale value changes of N pixels in the T-line region of each image in the first multi-exposure multi-brightness image set to obtain a grayscale value matrix of N×P×L pixels;

[0080] S3, constructing an initial two-parameter camera response function based on the grayscale value matrix, exposure time and current value;

[0081] S4. Construct the following piecewise weight function:

[0082]

[0083] Wherein, the function forms of f1(z) and f2(z) include linear function, quadratic polynomial function, exponential function or logarithmic function, and z1 and z2 are segmentation points;

[0084] S5. Determine the function forms of f1(z) and f2(z) and the segmentation points z1 and z2 that minimize the error between the average pixel value of the T-line region and the true pixel value, and obtain a target segmentation weight function; the average pixel value of the T-line region is calculated by: optimizing the initial two-parameter camera response function using the segmentation weight function; acquiring an HDR image according to the optimized camera response function, the second multi-exposure multi-brightness image set, the corresponding exposure time, and the corresponding current value; and performing tone mapping on the HDR image to obtain the average pixel value of the T-line region;

[0085] S6. Optimizing the initial two-parameter camera response function using the target segmented weight function to obtain a target two-parameter camera response function;

[0086] S7. According to the target dual-parameter camera response function, the multi-exposure and multi-brightness image set of the standard immunochromatographic test paper card, the corresponding exposure time and the corresponding current value, the average pixel value of the T-line area of ​​the standard immunochromatographic test paper card is obtained, and the average pixel value of the T-line area of ​​the standard immunochromatographic test paper card is fitted with the corresponding concentration to generate a standard concentration quantitative curve.

[0087] like Figure 1 As shown in FIG, the specific process can be divided into (1) initialization stage: establishing and optimizing the multi-exposure and multi-brightness dual-parameter response function curve of the image acquisition device; (2) use stage: using the image acquisition device to shoot a multi-exposure and multi-brightness image set of standard immunochromatographic test strips with different concentrations, using the established camera response function curve to guide the multi-exposure and multi-brightness image set to generate an HDR image, and then extracting the characteristic value of the region of interest (ROI) in the HDR image to expand the linear range of the standard quantitative curve.

[0088] In this solution, the standard immunochromatographic test paper cards include colloidal gold immunochromatographic cards and fluorescent immunochromatographic cards. The specific implementation methods of the two different test paper cards are different. Here, the colloidal gold immunochromatographic card is used as an example for description.

[0089] Before acquiring a multi-exposure and multi-brightness image set, the following steps are also included:

[0090] Design X standard immunochromatographic test strips with different T-line concentrations, and set the C-line concentration of each standard immunochromatographic test strip to be the same.

[0091] Use image processing software to make Figure 2 The standard immunochromatographic test strip card shown is designed with a quality control strip area frame of corresponding size, including the quality control line (C line) and the detection line (T line). The size of the quality control strip and the position of the C line and T line are cut to be exactly the same as the size and position of the actual test strip card product, and three test strips of the same concentration are produced at a time, reserved for subsequent repetitive tests.

[0092] After the standard colloidal gold immunochromatographic test paper card is designed, it is printed on Pantac 230g reinforced rough paper using a high-resolution color photocopying device. In this embodiment, a standard immunochromatographic test paper card with 36 different T-line grayscales ranging from 0 to 255 is designed with a pixel difference of 7, and the C-line pixel value of each test paper card is the same. Figure 3 As shown, it represents 36 standard colloidal gold immunochromatographic test strips with the same C line gray value and different T line gray values.

[0093] In this example, measurements on standard immunochromatographic test strips were performed in darkness. A sample set consisting of 36 test strips was captured at specific exposures and different current values ​​to cover a range of conditions, from underexposure to overexposure. The multi-exposure, multi-brightness image set covered pixel values ​​from 0 to 255 at different exposure time sequences. The measurements were also performed in darkness, with the light intensity adjusted by varying the current. A sample set consisting of 36 test strips was captured at three exposures to cover a range of conditions, from underexposure to overexposure.

[0094] In this embodiment, the value of P is 3 and the value of L is 5. Figure 4 As shown, the standard grayscale test paper card was photographed at 3 exposure times and 5 currents (0.122ms, 0.977ms, 31.25ms, 0.9mA, 1.7mA, 4.4mA, 8.5mA, 16.5mA), and a set of 36×3×5 multi-exposure photos from underexposure to overexposure was obtained. Figure 4 It shows 15 multi-exposure brightness photos taken with a 30# colloidal gold immunochromatographic test paper card.

[0095] like Figure 5As shown in (A), 8 test strips are evenly selected to cover the grayscale value range of 0-255 as much as possible, namely test strips card 1#, test strips card 5#, test strips card 10#, test strips card 15#, test strips card 20#, test strips card 25#, test strips card 30#, and test strips card 35#. These 8×3×5 images will be used to establish the dual-parameter response function curve.

[0096] After obtaining a set of multi-exposure and multi-brightness images, the images need to be pre-processed. The acquired immunochromatographic images are acquired by CMOS image sensors, and these images often contain noise. The shorter the exposure time, the more noise there is in the image. Figure 6 As shown, the multi-exposure and multi-brightness image set can be preprocessed using a median filter algorithm, a Gaussian filter algorithm, a bilateral filter algorithm, or a guided filter algorithm. In this embodiment, the most effective guided filter algorithm is used to preprocess the exposure image set, effectively reducing image noise and enhancing target visibility.

[0097] Optionally, S2 specifically includes:

[0098] S21 . Extracting a T-line region of each image in the first multi-exposure multi-brightness image set using an image segmentation algorithm.

[0099] The HDR image in .hdr format is converted into a high-quality LDR image through a tone mapping algorithm to adapt to the dynamic range of conventional display devices. Determine the position and size of the region of interest (such as the T-line area) in the standard immunochromatographic test strip card, extract the ROI area, and convert it into a single-channel grayscale image. Divide the image into two parts, upper and lower, at the vertical midpoint of the grayscale image, and apply sliding window threshold segmentation on the two parts. The window moves along the vertical direction of the image, and the average pixel value in each window is calculated. By comparing the pixel values ​​of different windows, find the area with the smallest pixel value, which corresponds to the T line and C line. The process of the sliding window threshold segmentation algorithm is as follows Figure 7 shown.

[0100] The segmented image is Figure 8 As shown, the T-line and C-line areas are clearly identified, and the average pixel values ​​of the two areas are calculated.

[0101] Alternatively, other threshold segmentation algorithms can be used, such as the maximum between-class variance (OTSU) algorithm.

[0102] S22, vertically stitching the T-line regions of each image in order of concentration to obtain a guide image;

[0103] S23 , obtaining grayscale value changes of N / X1 pixels in each T-line area in the guide image to obtain a grayscale value matrix of N×P×L pixels.

[0104] The T-line ROI areas of the eight immunochromatographic test strip card images taken at three exposure times and five currents are stitched together to form an image with eight color levels, which is called the guide image. Figure 8 As shown. 40 pixels are selected in the guide image. The changes in the pixel values ​​of these 40 pixels under 3 exposure times and 5 currents will be used to fit the dual-parameter response function curve. For a pixel value range of (Zmax-Zmin) = 255, and 15 dual-parameter photos per test card, select N = 40 > [255 / (15-1) = 19] pixels. Then, in each of the 8 T-line ROI areas, select Q = 40 / 8 = 5 pixels, as shown in the figure. Figure 5 As shown in (C).

[0105] Construct a weight function to optimize the camera response function curve. The form of the weight function needs to be determined first.

[0106] Standard concentration quantification curves in immunochromatography often exhibit S-shaped characteristics. In the middle of this curve, a linear relationship exists between the measured pixel value and the target substance concentration, while at the ends, the relationship plateaus and becomes less apparent. Therefore, a piecewise weighting function strategy is employed to more precisely reflect the characteristics of each concentration segment. The linear range of the standard concentration quantification curve is used to determine the boundaries between the linear and nonlinear intervals. The weight coefficient is set to 1 in the linear interval, preserving the linear characteristics of the original signal.

[0107] Changing the exposure time and light intensity at the same time requires combining two HDR algorithms to adjust the input matrix of current value and exposure time. In this case, a three-dimensional data set needs to be constructed, which contains pixel values, exposure time and current value. The exposure time set is denoted as Δt j , j = 1, 2, ..., P; the current set is recorded as I m , m=1,2,…,Q; use Z ijm represents the pixel value, where i = 1, 2, 3, ..., N, and represents the three-dimensional index of the location in the image. These three parameters work together to form the camera response function. The following are the general steps to adjust the algorithm to accommodate this change:

[0108] f is the brightness response function that needs to be solved, which can be expressed as:

[0109] Z=f(E,I,Δt) (1-1)

[0110] According to the formula of Z and I, let the mth current be I m , the irradiance of the i-th pixel is:

[0111] E im =E i I m (1-2)

[0112] I m is known, and k i is a constant because when the light intensity does not change, the irradiance of each pixel is constant. Let the pixel value of the i-th pixel at the m-th current and the j-th exposure time be Z ijm , formula (5-2) can be written as:

[0113] f -1 (Z ijm )=E im Δt j =E i I m Δt j (1-3)

[0114] G(Z ijm )=lnE i +lnI m +lnΔt j (1-4)

[0115] Need to restore irradiance E i and the estimated value of G, use the least squares method to solve the following objective function O dual , to minimize:

[0116]

[0117] Among them, I m and Δt j It is known that w(Z ijm ) is the weight function corresponding to the pixel point, so through the optimization algorithm, we can find the E that minimizes the objective function i and G values ​​to obtain the optimal brightness response function curve.

[0118] When the product of the “exposure time-current” pair is used as a whole to calculate the least square method to solve the objective function, that is, ln(I m ×Δt j ) as a variable to replace the exposure time variable lnΔt of the multi-exposure fusion HDR algorithm j Or the current variable ln(I m ×Δt j ), a "single parameter" response curve fitting was performed. Because exposure time and pixel irradiance are independent parameters, they do not affect each other. At this time, formula (1-5) can be transformed into:

[0119]

[0120] The multi-exposure and multi-brightness image sets were taken at three different exposure times and five different currents and were calculated according to the formula:

[0121]

[0122] Therefore, at least 19 pixels need to be selected to meet the minimum requirement of the number of pixels required for fitting the dual-parameter response function curve. In this embodiment, 50 pixels are selected to fit the dual-parameter camera response function curve of the colloidal gold immunochromatographic image acquisition device.

[0123] A 3D dual-parameter response function graph is established, with the logarithm of exposure time set as the X-axis, the logarithm of current set as the Y-axis, and the dual-parameter response function value G set as the Z-axis. In this way, the position of any point in this three-dimensional space can reflect the brightness value under a specific exposure time and current value. After calculation by Python software, the 3D dual-parameter response function graph of the colloidal gold immunochromatography image acquisition device is obtained as shown below: Figure 9 As shown in (A), Figure 9 (B) and (C) show the response curves of the device at different currents and exposure times:

[0124] This embodiment analyzes the application of identity function, hat function and Gaussian function in camera response function curve fitting, and obtains the following Figure 10 The five pixels shown have different exposure offsets calculated under different weight functions and Figure 11 The three response function curves g are shown.

[0125] Among them, the identity function form is:

[0126] w1(z)=1,z∈[0,255]

[0127] The functional form of the hat function is:

[0128]

[0129] The functional form of the Gaussian function is:

[0130]

[0131] Among them, parameter a controls the shape of the Gaussian function. A larger a value results in a wider range of pixels with a weight value of 1 in the weight function. Parameter b controls the width of the Gaussian function. A larger b value makes the distribution range of pixels with a weight value greater than 0 narrower.

[0132] pass Figure 11The experimental results are obtained, and the residuals of the camera response function curves obtained under three different weight functions, namely the identity function, the hat function and the Gaussian function, are calculated. They are 6.24503, 0.27919 and 0.19764 respectively. The experimental results show that the fitting degree of the CRF curve under the Gaussian function is better than that of the curves fitted under the identity function and the hat function.

[0133] Furthermore, different (a, b) parameter combinations of the Gaussian function will fit different CRF results, and different fitting results will directly affect the pixel value of each pixel in the HDR image. Therefore, this embodiment establishes a parameter selection algorithm to optimize the selection of a and b of the Gaussian function. The a and b of the Gaussian function are respectively taken within a certain interval, where a is between [2, 50] and a is an even number, and b is between [1, 4]. Because when a = 50 and b = 4, the Gaussian function has set the weights of most pixel values ​​to 1, which is similar to the identity function.

[0134] The MAPE and RMSE values ​​between the pixel values ​​of the T line in the HDR image corrected by the CRF curve fitted with different parameters a and b and the actual T line printed grayscale value are calculated. By comparing the MAPE and RMSE results of 36 test strips under different a and b parameter combinations, the optimal CRF curve is determined, which makes the pixel values ​​of the T line in the corrected HDR image closer to the actual printed grayscale value. The comparison results are shown in Figure 2. Figure 12 shown.

[0135] Experimental results show that when the parameter combination is (a=10, b=4), the obtained MAPE and RMSE values ​​reach their minimum values, at 41.16% and 11.42, respectively. This indicates that under this parameter combination, the CRF curve fitted by the Gaussian function can effectively correct the HDR image, making its T-line pixel values ​​closest to the actual printed grayscale values.

[0136] According to the optimal function form of the Gaussian function, the following is drawn: Figure 13 As shown in the weight function diagram, it can be found that the weight values ​​begin to turn when the pixel values ​​are 50 and 200, which means that these two pixel values ​​approximately mark the end of the low-brightness area and the beginning of the high-brightness area in the response function.

[0137] So we construct a piecewise function that behaves differently in low-brightness and high-brightness areas. The functional form of the piecewise function is:

[0138]

[0139] Wherein, the function forms of f1(z) and f2(z) include linear function, quadratic polynomial function, exponential function or logarithmic function, and z1 and z2 are segmentation points;

[0140] After determining the form of the piecewise weight function, it is necessary to solve the piecewise weight function. First, set the values ​​of the initial segmentation points z1 and z2 to obtain the first piecewise weight function. In this embodiment, the value of segmentation point z1 is set to 50 and the value of segmentation point z2 is set to 200. Based on this, the appropriate functions f1(z) and f2(z) are selected.

[0141] Functions such as linear, quadratic polynomial, exponential, and logarithmic are selected as candidates for f1(z) and f2(z). The current camera response function is derived from the first segmented weight function combined with the grayscale matrix. The mean absolute percentage error (MAPE) and root mean square error (RMSE) between the pixel values ​​of the T-line in the HDR image and the actual printed grayscale values ​​of the T-line are calculated by correcting the camera response function curve. The function types of f1(z) and f2(z) are changed, and the first segmented weight function is updated to update the camera response function. The calculated MAPE and RMSE values ​​are updated. Using MAPE and RMSE as the objective function, the f1(z) and f2(z) values ​​that minimize MAPE and RMSE are solved as the final expression of the segmented function. The optimal segmented function form is found and compared with the optimal function form of the Gaussian function.

[0142] Specifically, for the calculation of the weight function, this embodiment uses multi-exposure images of 36 test strips at 9 exposure times. The pixel values ​​of the 36 test strips at 9 exposure times are divided into 9 groups. Figure 14 Middle (A). The T-line pixel values ​​of test strips 1-4 (group 1, and so on) were captured at 31.25ms, the T-line pixel values ​​of test strips 5-8 were captured at 15.625ms, the T-line pixel values ​​of test strips 9-12 were captured at 7.812ms, the T-line pixel values ​​of test strips 13-16 were captured at 3.906ms, the T-line pixel values ​​of test strips 17-20 were captured at 1.953ms, the T-line pixel values ​​of test strips 21-24 were captured at 0.977ms, the T-line pixel values ​​of test strips 25-28 were captured at 0.488ms, the T-line pixel values ​​of test strips 29-32 were captured at 0.244ms, and the T-line pixel values ​​of test strips 33-36 were captured at 0.122ms. Figure 14 (B) shows the slope of the mapping between the T-line pixel values ​​of each test strip and the actual printed grayscale values. This slope reflects the camera's sensitivity to grayscale value changes at a specific exposure time. Groups with higher slopes demonstrate high sensitivity to grayscale value changes, while groups with lower slopes indicate reduced sensitivity to grayscale value changes within this range.

[0143] The optimal f1(z) and f2(z) function forms were selected by adjusting the piecewise function form based on the MAPE and RMSE values. The MAPE and RMSE values ​​calculated for different piecewise functions are shown in Table 1. Experimental results show that when f1(x) is an exponential function and f2(x) is a linear function, the MAPE and RMSE values ​​are minimized, indicating that this piecewise function combination is able to achieve optimal HDR image correction. The MAPE of the T-line pixel values ​​extracted from the original image without HDR processing and the T-line printed grayscale values ​​was calculated, reaching 117%.

[0144] Table 1 Error results of different f1(x) and f2(x) in piecewise function

[0145]

[0146]

[0147] After updating the expression of the first segment weight function, it is necessary to optimize the value range of the segment points. The search range of z1 and z2 is related to the performance of the CMOS image sensor used. The search range of segment points z1 and z2 is set to obtain the second segment weight function. In this embodiment, by observing Figure 20 For the T-line pixel values ​​of the digital image of the test paper card, when the printed grayscale of the test paper card is around 50, the slope of the multi-exposure pixel value becomes 0.9, which is close to linear, indicating good camera response at this time. Therefore, the search range of z1 is set to 40-70 (grayscale value). For the test paper card with a high printed grayscale value, the slope decreases to 0.74 at a pixel value of around 200, so the search range of z2 is set to 190-230 (grayscale value).

[0148] A cycle is performed on z1 and z2 of the segmented weight function, with a step size of 1. The second segmented weight function is gradually updated, and the MAPE and RMSE values ​​between the pixel values ​​of the T line in the HDR image corrected with different z1 and z2 and the actual printed T line grayscale values ​​are calculated. Based on the minimum MAPE and RMSE results, the segmentation point of the target weight function is determined. Through the optimization algorithm, when z1 = 50 and z2 = 200, the MAPE of 36 test strips is 36.21% and the RMSE is 11.23, achieving the minimum error. This result is better than the MAPE = 41.16% and RMSE = 11.42 calculated using the Gaussian function objective function form. Therefore, the form of the target weight function in this embodiment is as follows:

[0149]

[0150] After obtaining the target segment weight function, the target two-parameter camera response function can be obtained according to the target segment weight function combined with the gray value matrix.

[0151] Take the "exposure time-current" product pair as a whole, and input the image set taken under each product pair to solve the objective function in formula (1-6). The pixel value of the digital image is the horizontal coordinate, and lnE is the horizontal coordinate. i +ln(I m ×Δt j ) is the vertical axis, and a 2D dual-parameter response function curve is drawn. The 2D dual-parameter response function diagram of the colloidal gold immunochromatography microscopic image acquisition device is obtained as shown in FIG. Figure 15 shown.

[0152] Optionally, S7 specifically includes:

[0153] S71, obtaining an average pixel value of the T-line region of the standard immunochromatographic test strip card according to the target dual-parameter camera response function, the multi-exposure and multi-brightness image set of the standard immunochromatographic test strip card, the corresponding exposure time, and the corresponding current value;

[0154] S72. With the average pixel value in S71 as the y-axis and the actual concentration of the standard immunochromatographic test paper card as the x-axis, a four-parameter logistic fit is used to obtain a standard concentration quantitative curve.

[0155] High dynamic range radiation pattern k i Restore as follows:

[0156]

[0157] Once the dual-parameter response function curve G of the colloidal gold immunochromatography imaging device is successfully restored, the Figure 3 36 colloidal gold immunochromatographic test strips with different printing concentrations and T values ​​are used to realize the operation of generating HDR images from dual-parameter LDR image sets.

[0158] Figure 16 The image shows 15 multi-exposure brightness images taken with a 30# colloidal gold immunochromatographic test strip, along with an HDR image synthesized using the DPRF curve. The contrast and C- and T-line clarity of the colloidal gold immunochromatographic HDR image are superior to those of the 15 dual-parameter LDR images.

[0159] The four-parameter logistic fitting model is usually expressed as follows:

[0160]

[0161] Among them, A1 represents the maximum eigenvalue of the curve, that is, the maximum average pixel value of the T line in the measurement image; A2 represents the minimum eigenvalue of the curve, that is, the minimum average pixel value of the T line in the measurement image; x represents the independent variable, that is, the printed grayscale value of the T line; x0 is the EC50 (half-maximal effect concentration), that is, the printed grayscale value corresponding to the middle pixel value of the T line in the measurement image; p controls the slope of the curve. At the same time, the four-parameter logistic fitting model has an important evaluation parameter, the goodness of fit R 2 This value reflects the goodness of fit of the quantitative curve of the standard concentration. The closer its value is to 1, the more accurate the fitted curve is. By fitting the experimental data to a four-parameter logistic model, these parameters can be estimated and an S-shaped curve can be obtained that fits the data well.

[0162] After obtaining the standard concentration quantitative curve, it also includes:

[0163] The paper card to be tested is photographed in a multi-exposure time sequence and at different light intensities to obtain a multi-exposure and multi-brightness image set of the paper card to be tested;

[0164] The actual concentration value of the paper card to be tested is obtained according to the standard concentration quantitative curve, the multi-exposure and multi-brightness image set of the paper card to be tested, the corresponding exposure time and the corresponding current value.

[0165] The results of 30 test strips were selected to fit the standard concentration quantitative curve, such as Figure 22 As shown, the horizontal axis is the grayscale value of the T line print of 30 test strips, and the vertical axis is the change of the T line pixel value in the HDR image. By observing the curve, the data points are closely distributed around the fitting curve, and the fitting degree is 0.99953, which means the fitting effect is very good.

[0166] In order to study the effectiveness of the algorithm designed in this study, the standard concentration quantitative curves of the colloidal gold immunochromatography microscopy image acquisition device before and after the HDR algorithm was used were compared. That is, the T-line pixel values ​​of 30 test strip card LDR images taken under normal exposure (1.953ms) were compared with the T-line pixel values ​​of 36 HDR images synthesized using the HDR algorithm, and the upper and lower limits of the measurable signal value area S of the two standard concentration quantitative curves were plotted. The results are shown in Figure 2. Figure 17 As shown, before the HDR algorithm, the range of test strips 5# to 34# could be measured, while after the HDR algorithm, the range was expanded to test strips 3# to 36#, demonstrating the effectiveness of the dual-parameter fusion HDR algorithm in expanding the range of immunochromatographic images. In addition, the linear measurement range of the standard concentration quantitative curve has also been significantly expanded.

[0167] The specific implementation method for the standard immunochromatographic test paper card being a fluorescent immunochromatographic card includes the following steps:

[0168] like Figure 18As shown in the figure, 7 fluorescent immunochromatographic test strips with different fluorescence concentrations were made.

[0169] Measurements in this example were performed in darkness. By varying the current to adjust the illumination intensity, a sample set consisting of seven test strips was captured at three exposure times, covering a range from underexposure to overexposure. This example used fluorescent color-scale test strips with three exposure times and five currents (0.008ms, 0.033ms, and 0.125ms; 58.6mA, 91.5mA, 129.5mA, 166.6mA, and 206.1mA), resulting in a 7×3×5 multi-exposure set of images ranging from underexposure to overexposure. Because the fluorescent images captured are in RGB (red, green, and blue) format, and the three RGB channels are stored as three two-dimensional channels in a computer, standard RGB is 24-bit, with each component occupying 8 bits. Because the emission wavelength of fluorescent microspheres is at 610nm, which is within the red light wavelength range, according to the characteristics of fluorescence images, it is necessary to first extract the red component image of each image to obtain an image containing more fluorescence information and less noise in order to continue image segmentation and feature extraction. Figure 19 It shows 15 multi-exposure brightness photos taken of the B4 fluorescent immunochromatographic test paper card.

[0170] The T-line ROI areas of the non-feature values ​​of the 7 fluorescent immunochromatographic test strip images taken at 3 exposure times and 5 currents are spliced ​​to form an image with 12 different color levels, which is called the guide image, such as Figure 20 shown.

[0171] After acquiring the multi-exposure, multi-brightness image set, the images need to be preprocessed. After preprocessing, the images undergo segmentation and localization to extract feature values. The preprocessing process and segmentation, localization, and extraction process are the same as those described in the colloidal gold immunochromatography card embodiment and will not be repeated here.

[0172] Changing both exposure time and light intensity requires combining two HDR algorithms for adjustment, inputting a matrix of current values ​​and exposure times. The calculation of the dual-parameter camera response function is the same as in the colloidal gold immunochromatography card embodiment above and will not be repeated here.

[0173] A 3D dual-parameter response function graph is established, with the logarithm of exposure time set as the X-axis, the logarithm of current set as the Y-axis, and the dual-parameter response function value G set as the Z-axis. In this way, the position of any point in this three-dimensional space can reflect the brightness value under a specific exposure time and current value. After calculation by Python software, the 3D dual-parameter response function graph of the fluorescence immunochromatography image acquisition device is finally obtained as shown below: Figure 21 As shown in (A), Figure 21 (B) and (C) show the response curves of the device at different currents and exposure times.

[0174] The process of solving the piecewise weight function is similar to that of the embodiment of the colloidal gold immunochromatography card, and will not be repeated here.

[0175] Therefore, the objective weight function in this embodiment is in the following form:

[0176]

[0177] Take the "exposure time-current" product pair as a whole, and input the image set taken under each product pair to solve the objective function in formula (1-6). The pixel value of the digital image is the horizontal coordinate, and lnE is the horizontal coordinate. i +ln(I m ×Δt j ) is the vertical axis, and a 2D dual-parameter response function curve is drawn. The 2D dual-parameter response function diagram of the colloidal gold immunochromatography microscopic image acquisition device is obtained as shown in FIG. Figure 22 shown.

[0178] High dynamic range radiation pattern k i Restore as follows:

[0179]

[0180] Once the dual-parameter response function curve G of the fluorescence immunochromatography device is successfully restored, the Figure 10 Seven fluorescent immunochromatographic test strips with different concentrations are used to realize the operation of generating HDR images from dual-parameter LDR image sets.

[0181] Figure 23 The figure shows 15 multi-brightness photos taken of the B4 fluorescent immunochromatographic test card and the HDR image synthesized under the DPRF curve.

[0182] The four-parameter logistic fitting of the standard concentration quantitative curve of the immunochromatography is similar to the embodiment of the colloidal gold immunochromatography card described above and will not be described in detail here.

[0183] After obtaining a set of seven dual-parameter HDR images of fluorescent immunochromatographic analysis, a sliding window algorithm was used to segment and extract the T-line of the images. The average T-line pixel values ​​were recorded in Table 2 and recorded as "T-line after HDR." For the fluorescent immunochromatographic test strips, fluorescence values ​​were measured on the B1-B7 fluorescent test strips using a commercial fluorescent immunochromatographic instrument before testing. These values ​​were recorded as "quantitative T-line by commercial instrument." The average T-line pixel value of the LDR images of the seven fluorescent immunochromatographic test strips, taken at 129.5 mA and 0.033 ms, was calculated and recorded as "T-line before HDR."

[0184] Table 2 Average pixel values ​​of T lines before and after HDR algorithm for fluorescent immunochromatographic test strips

[0185]

[0186] Figure 24 In the figure (A), the horizontal axis represents the fluorescence intensity of the T lines measured on seven fluorescent immunochromatographic test strips using a fluorescent immunoassay analyzer. The vertical axis of the black fitting curve represents the average pixel value of the T lines before and after the dual-parameter HDR algorithm is used. The results show that the dual-parameter HDR algorithm can improve the fit of the standard concentration quantitative curve from 0.97399 to 0.99081. Figure 24 Middle (B) compares the linear range before and after using the dual-parameter HDR algorithm. The results show that after using the dual-parameter HDR algorithm, the linear range of detection can be expanded from 1200 to 10000 (fluorescence characteristic value) to 600 to 18000 (fluorescence characteristic value), and the fitting degree reaches 0.99421.

[0187] In the process of restoring the dual-parameter camera response function of the image acquisition system, the embodiments of the present invention employ a piecewise weighting function to adjust the weights of images with different exposures and current values, precisely adjusting the fusion of multi-exposure and multi-brightness images and mitigating the impact of overexposed and underexposed areas in the image on the accuracy of the camera response function fitting. This allows the fitted standard quantitative curve to align with the characteristics of the immunochromatographic S-shaped curve, capturing a wider brightness range at different exposure levels and current values. This effectively overcomes the limitation of loss of highlight or shadow detail in single-exposure or single-brightness images and significantly improves the accuracy of HDR image synthesis. The effects of image noise and quantization error are effectively reduced, making the synthesized immunochromatographic image closer to the real scene and the analysis and detection results based on the immunochromatographic test strip more accurate. This solves the technical problem of the limited dynamic range of photos in traditional HDR technology, which leads to a narrow detection range, slow detection speed, and low detection accuracy in immunochromatographic test instruments based on image imaging principles. This improves detection speed and accuracy, and expands the scope of immunochromatographic test strip analysis and detection.

[0188] Example 2

[0189] Based on the above embodiments, the present invention further provides an analysis and detection device based on multi-exposure and multi-brightness dual parameters, characterized in that it is used to perform the analysis and detection method as described in any one of the first embodiments, including:

[0190] An image acquisition module is used to photograph the paper card to be tested under multiple exposure time sequences and different light intensities to obtain a set of multi-exposure and multi-brightness images of the paper card to be tested;

[0191] A concentration analysis and detection module is used to obtain the actual concentration value of the paper card to be tested based on the standard concentration quantitative curve, the multi-exposure and multi-brightness image set of the paper card to be tested, the corresponding exposure time and the corresponding current value;

[0192] The method for generating the standard concentration quantitative curve includes:

[0193] Taking images of X standard immunochromatographic test strips of different concentrations at P multi-exposure time sequences and L light intensities to obtain a multi-exposure, multi-brightness image set; dividing the multi-exposure, multi-brightness image set into a first multi-exposure, multi-brightness image set including X1 images of the standard immunochromatographic test strips and a second multi-exposure, multi-brightness image set including X2 images of the standard immunochromatographic test strips; wherein P ≥ 3, L ≥ 3, and X = X1 + X2;

[0194] Obtaining grayscale value changes of N pixels in the T-line region of each image in the first multi-exposure multi-brightness image set to obtain a grayscale value matrix of N×P×L pixels;

[0195] constructing an initial two-parameter camera response function based on the grayscale value matrix, exposure time and current value;

[0196] Construct the following piecewise weight function:

[0197]

[0198] Wherein, the function forms of f1(z) and f2(z) include linear function, quadratic polynomial function, exponential function or logarithmic function, and z1 and z2 are segmentation points;

[0199] Determining the function forms of f1(z) and f2(z) and the segmentation points z1 and z2 that minimize the error between the average pixel value of the T-line region and the true pixel value, and obtaining a target segmentation weight function; calculating the average pixel value of the T-line region includes: optimizing the initial two-parameter camera response function using the segmentation weight function; acquiring an HDR image based on the optimized camera response function, a second multi-exposure multi-brightness image set, corresponding exposure time, and corresponding current value; performing tone mapping on the HDR image, and obtaining the average pixel value of the T-line region;

[0200] The target segmented weight function is used to optimize the initial two-parameter camera response function to obtain the target two-parameter camera response function;

[0201] According to the target dual-parameter camera response function, the multi-exposure and multi-brightness image set of the standard immunochromatographic test strip card, the corresponding exposure time and the corresponding current value, the average pixel value of the T-line area of ​​the standard immunochromatographic test strip card is obtained. The average pixel value of the T-line area of ​​the standard immunochromatographic test strip card is fitted with the corresponding concentration to generate a standard concentration quantitative curve.

[0202] An analysis and detection device based on multi-exposure and multi-brightness dual parameters provided in an embodiment of the present invention is used to execute an analysis and detection method based on multi-exposure and multi-brightness dual parameters provided in any embodiment 1 of the present invention, and has corresponding functional modules and beneficial effects.

[0203] It will be easily understood by those skilled in the art that the above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A dual-parameter analysis and detection method based on multi-exposure and multi-brightness, characterized in that: include: The paper card to be tested is photographed in a multi-exposure time sequence and at different light intensities to obtain a multi-exposure and multi-brightness image set of the paper card to be tested; According to the standard concentration quantitative curve, the multi-exposure and multi-brightness image set of the paper card to be tested, the corresponding exposure time and the corresponding current value, the actual concentration value of the paper card to be tested is obtained; The method for generating the standard concentration quantitative curve includes: S1. Capture X images of standard immunochromatographic test strips with different concentrations in P multi-exposure time sequences and L light intensities to obtain a multi-exposure, multi-brightness image set; divide the multi-exposure, multi-brightness image set into a first multi-exposure, multi-brightness image set including X1 images of the standard immunochromatographic test strips and a second multi-exposure, multi-brightness image set including X2 images of the standard immunochromatographic test strips; wherein P ≥ 3, L ≥ 3, and X = X1 + X2; S2. Obtain grayscale value changes of N pixels in the T-line region of each image in the first multi-exposure multi-brightness image set to obtain a grayscale value matrix of N×P×L pixels; S3, constructing an initial two-parameter camera response function based on the grayscale value matrix, exposure time and current value; S4. Construct the following piecewise weight function: Wherein, the function forms of f1(z) and f2(z) include linear function, quadratic polynomial function, exponential function or logarithmic function, and z1 and z2 are segmentation points; S5. Determine the function forms of f1(z) and f2(z) and the segmentation points z1 and z2 that minimize the error between the average pixel value of the T-line region and the true pixel value, and obtain a target segmentation weight function; the average pixel value of the T-line region is calculated by: optimizing the initial two-parameter camera response function using the segmentation weight function; acquiring an HDR image according to the optimized camera response function, the second multi-exposure multi-brightness image set, the corresponding exposure time, and the corresponding current value; and performing tone mapping on the HDR image to obtain the average pixel value of the T-line region; S6. Optimizing the initial two-parameter camera response function using the target segmented weight function to obtain a target two-parameter camera response function; S7. According to the target dual-parameter camera response function, the multi-exposure and multi-brightness image set of the standard immunochromatographic test paper card, the corresponding exposure time and the corresponding current value, the average pixel value of the T-line area of ​​the standard immunochromatographic test paper card is obtained, and the average pixel value of the T-line area of ​​the standard immunochromatographic test paper card is fitted with the corresponding concentration to generate a standard concentration quantitative curve.

2. The analysis and detection method according to claim 1, wherein The standard immunochromatographic test paper cards have different T line concentrations and the same C line concentration.

3. The analysis and detection method according to claim 1, wherein S2 specifically includes: S21, extracting a T-line region of each image in the first multi-exposure multi-brightness image set using an image segmentation algorithm; S22, vertically stitching the T-line regions of each image in order of concentration to obtain a guide image; S23 , obtaining grayscale value changes of N / X1 pixels in each T-line area in the guide image to obtain a grayscale value matrix of N×P×L pixels.

4. The analysis and detection method according to claim 1, wherein The error between the average pixel value and the true pixel value is the root mean square error RMSE and the mean absolute percentage error MAPE of the average pixel value of the T-line area and the true pixel value of the standard immunochromatographic test paper card.

5. The analysis and detection method according to claim 1, wherein S7 specifically includes: S71, obtaining an average pixel value of the T-line region of the standard immunochromatographic test strip card according to the target dual-parameter camera response function, the multi-exposure and multi-brightness image set of the standard immunochromatographic test strip card, the corresponding exposure time, and the corresponding current value; S72. With the average pixel value in S71 as the y-axis and the actual concentration of the standard immunochromatographic test paper card as the x-axis, a four-parameter logistic fit is used to obtain a standard concentration quantitative curve.

6. The analysis and detection method according to claim 1, wherein The standard immunochromatographic test paper cards include colloidal gold immunochromatographic cards and fluorescent immunochromatographic cards.

7. The analysis and detection method according to claim 1, wherein The multi-exposure and multi-brightness image set covers pixel values ​​0-255 at different exposure time sequences and light intensities.

8. The analysis and detection method according to claim 1, wherein After obtaining the multi-exposure multi-brightness image set, the method further includes: pre-processing the multi-exposure multi-brightness image set using a median filtering algorithm, a Gaussian filtering algorithm, a bilateral filtering algorithm or a guided filtering algorithm.

9. An analysis and detection device based on multi-exposure and multi-brightness dual parameters, characterized in that: Used to perform the analysis and detection method according to any one of claims 1 to 8, comprising: An image acquisition module is used to photograph the paper card to be tested under multiple exposure time sequences and different light intensities to obtain a set of multi-exposure and multi-brightness images of the paper card to be tested; A concentration analysis and detection module is used to obtain the actual concentration value of the paper card to be tested based on the standard concentration quantitative curve, the multi-exposure and multi-brightness image set of the paper card to be tested, the corresponding exposure time and the corresponding current value; The method for generating the standard concentration quantitative curve includes: Taking images of X standard immunochromatographic test strips of different concentrations at P multi-exposure time sequences and L light intensities to obtain a multi-exposure, multi-brightness image set; dividing the multi-exposure, multi-brightness image set into a first multi-exposure, multi-brightness image set including X1 images of the standard immunochromatographic test strips and a second multi-exposure, multi-brightness image set including X2 images of the standard immunochromatographic test strips; wherein P ≥ 3, L ≥ 3, and X = X1 + X2; Obtaining grayscale value changes of N pixels in the T-line region of each image in the first multi-exposure multi-brightness image set to obtain a grayscale value matrix of N×P×L pixels; constructing an initial two-parameter camera response function based on the grayscale value matrix, exposure time and current value; Construct the following piecewise weight function: Wherein, the function forms of f1(z) and f2(z) include linear function, quadratic polynomial function, exponential function or logarithmic function, and z1 and z2 are segmentation points; Determining the function forms of f1(z) and f2(z) and the segmentation points z1 and z2 that minimize the error between the average pixel value of the T-line region and the true pixel value, and obtaining a target segmentation weight function; calculating the average pixel value of the T-line region includes: optimizing the initial two-parameter camera response function using the segmentation weight function; acquiring an HDR image based on the optimized camera response function, a second multi-exposure multi-brightness image set, corresponding exposure time, and corresponding current value; performing tone mapping on the HDR image, and obtaining the average pixel value of the T-line region; The target segmented weight function is used to optimize the initial two-parameter camera response function to obtain the target two-parameter camera response function; According to the target dual-parameter camera response function, the multi-exposure and multi-brightness image set of the standard immunochromatographic test strip card, the corresponding exposure time and the corresponding current value, the average pixel value of the T-line area of ​​the standard immunochromatographic test strip card is obtained. The average pixel value of the T-line area of ​​the standard immunochromatographic test strip card is fitted with the corresponding concentration to generate a standard concentration quantitative curve.

Images