Particle image reconstruction method and device, computer equipment and storage medium

Abstract

The invention relates to a particle image reconstruction method and device, computer equipment and a storage medium. The method comprises the following steps: collecting original pulse image data, carrying out integral imaging processing on the original pulse image data by adopting different integral times for multiple times, and determining a target region comprising particles and a background region not comprising particles in the original pulse image data; determining first integral time corresponding to each target area according to the signal-to-noise ratio of each target area corresponding to different integral time, and determining second integral time according to the brightness of the background area in each integral imaging result; and performing integral imaging processing on each target area according to the first integral time, performing integral imaging processing on the background area according to the second integral time, and performing reconstruction according to integral imaging results of each target area and the background area to obtain multiple frames of target particle images corresponding to the particle area. By adopting the method, the particle image reconstruction precision can be improved.

Description

Technical Field

[0001] The present application relates to the field of image processing, and in particular to a particle image reconstruction method, apparatus, computer equipment, storage medium and computer program product. Background Art

[0002] Particle Image Velocimetry (PIV) technology plays a vital role in fields such as fluid mechanics and aerodynamics. It can provide quantitative measurement of the velocity distribution of the flow field, which is crucial for understanding the laws of fluid motion, optimizing fluid machinery design, and studying various fluid phenomena.

[0003] PIV technology usually uses a high-speed camera to capture particle images in the flow field area. However, in flow fields with strong brightness contrast (such as combustion flow fields), the particle images captured by the camera are usually overexposed or underexposed, resulting in oversaturation of particle images in bright areas, and weak particle signals in dark areas, resulting in insufficient particle signal-to-noise ratio, causing image information in some areas to be distorted or lost. Therefore, traditional particle image acquisition technology has the problem of insufficient accuracy of the collected particle images. Summary of the invention

[0004] Based on this, it is necessary to provide a particle image reconstruction method, device, computer equipment and storage medium to address the above technical problems.

[0005] In a first aspect, the present application provides a particle image reconstruction method. The method comprises:

[0006] Collecting raw pulse image data for a particle region within a preset sampling time, performing integral imaging processing on the raw pulse image data for multiple times using different integral times, and determining a target region including particles and a background region excluding particles in the raw pulse image data according to each integral imaging result obtained;

[0007] Determine the signal-to-noise ratio of each target area corresponding to different integration times, determine the first integration time corresponding to each target area according to each signal-to-noise ratio, and determine the second integration time corresponding to the background area according to the brightness of the background area in each integral imaging result;

[0008] Integral imaging processing is performed on each of the target areas according to the first integration time, and integral imaging processing is performed on the background area according to the second integration time. Based on the integral imaging results of each of the target areas and the background area, a multi-frame target particle image corresponding to the particle area is reconstructed.

[0009] In one embodiment, the step of performing integral imaging processing on the raw pulse image data for multiple times using different integral times, and determining a target area including particles and a background area not including particles in the raw pulse image data according to each integral imaging result obtained, comprises:

[0010] The original pulse image data is used as target pulse image data, and the target pulse image data is subjected to integral imaging processing using a target integration time to obtain an integral imaging result, and a particle region including particles is determined from the integral imaging result, and a portion of the original pulse image data corresponding to the particle region is used as a target region;

[0011] The part of the original pulse image data except the target areas is used as the target pulse image data, and the target integration time is increased, and the process jumps to the step of performing integral imaging processing on the target pulse image data using the target integration time to obtain an integral imaging result, until all the pixels in the integral imaging result are saturated;

[0012] The portion of the original pulse image data corresponding to the current target pulse image data is used as the background area.

[0013] In one embodiment, determining the signal-to-noise ratios of the target regions corresponding to different integration times, and determining the first integration time corresponding to the target regions according to the signal-to-noise ratios, includes:

[0014] Performing integral imaging processing on each of the target areas using different integral times to obtain a plurality of integral imaging results for each of the target areas, and determining a signal-to-noise ratio of each of the integral imaging results;

[0015] For any of the target areas, based on the signal-to-noise ratio corresponding to each of the integrated imaging results of the target area and the integration time corresponding to each of the integrated imaging results of the target area, a signal-to-noise ratio variation curve of the signal-to-noise ratio of the target area with respect to the integration time is constructed, and the integration time corresponding to the maximum value in the signal-to-noise ratio variation curve is used as the first integration time.

[0016] In one embodiment, determining the second integration time corresponding to the background area according to the brightness of the background area in each of the integral imaging results includes:

[0017] According to the pixel points in the background area and whose brightness reaches saturation in each of the integral imaging results, respectively determining the saturation integration time when the brightness of each of the pixel points in the background area reaches saturation;

[0018] According to the overexposure suppression strategy and the saturation integration time, the second integration time corresponding to each of the pixel points is determined respectively.

[0019] In one embodiment, reconstructing a multi-frame target particle image corresponding to the particle area according to the integrated imaging results of each of the target area and the background area includes:

[0020] Determine, according to the pulse sampling time and the preset sampling duration, each imaging moment corresponding to the particle region within the preset sampling duration;

[0021] Respectively determining a first integral imaging result corresponding to the integral imaging result of the background area at each of the imaging moments, and respectively determining a second integral imaging result corresponding to the integral imaging result of the target area at each of the imaging moments;

[0022] Based on the first integral imaging result and the second integral imaging result, target particle images corresponding to each imaging moment are generated respectively.

[0023] In one embodiment, the method further comprises:

[0024] Based on the pulse emission frequency corresponding to each pixel point in the original pulse image data, the pixel difference between each two adjacent target particle images, and at least one of the first target filter and the second target filter, denoising each target particle image to obtain a denoised target particle image;

[0025] The first target filter is constructed based on the target shape characteristics of the particles, and the second target filter is constructed based on the target motion characteristics of the particles.

[0026] In one embodiment, performing denoising on each of the target particle images based on the pixel difference between each two adjacent initial particle images comprises:

[0027] A difference processing is performed on any two adjacent frames of target particle images. When the pixel difference corresponding to any pixel point in the two frames of target particle images is greater than a first preset threshold and less than a second preset threshold, the pixel point is determined as a noise pixel point, and denoising is performed on each of the noise pixel points in the two frames of target particle images.

[0028] In one embodiment, the target shape feature of the particle includes an area feature and an intensity feature, wherein the area feature is that the number of pixel points corresponding to a single particle in the particle image is greater than 1, and the intensity feature is that among all the pixel points corresponding to a single particle in the particle image, the intensity of the central pixel point is greater than the intensity of the edge pixel point;

[0029] The target motion feature of the particle is that in the original pulse image data, the change values ​​of the pulse emission frequencies of a plurality of adjacent pixel points have a correlation relationship.

[0030] In a second aspect, the present application also provides a particle image reconstruction device. The device comprises:

[0031] An integration module is used to collect raw pulse image data for a particle region within a preset sampling time, perform integral imaging processing on the raw pulse image data using different integral times for multiple times, and determine a target region including particles and a background region excluding particles in the raw pulse image data according to each integral imaging result obtained;

[0032] A determination module, used to determine the signal-to-noise ratio of each target area corresponding to different integration times, determine the first integration time corresponding to each target area according to each signal-to-noise ratio, and determine the second integration time corresponding to the background area according to the brightness of the background area in each integral imaging result;

[0033] A reconstruction module is used to perform integral imaging processing on each of the target areas according to the first integration time, and to perform integral imaging processing on the background area according to the second integration time, and to reconstruct a multi-frame target particle image corresponding to the particle area based on the integral imaging results of each of the target areas and the background area.

[0034] In one embodiment, the integration module is further used for:

[0035] The original pulse image data is used as target pulse image data, and the target pulse image data is subjected to integral imaging processing using a target integration time to obtain an integral imaging result, and a particle region including particles is determined from the integral imaging result, and a portion of the original pulse image data corresponding to the particle region is used as a target region;

[0036] The part of the original pulse image data except the target areas is used as the target pulse image data, and the target integration time is increased, and the process jumps to the step of performing integral imaging processing on the target pulse image data using the target integration time to obtain an integral imaging result, until all the pixels in the integral imaging result are saturated;

[0037] The portion of the original pulse image data corresponding to the current target pulse image data is used as the background area.

[0038] In one embodiment, the determining module is further used to:

[0039] Performing integral imaging processing on each of the target areas using different integral times to obtain a plurality of integral imaging results for each of the target areas, and determining a signal-to-noise ratio of each of the integral imaging results;

[0040] For any of the target areas, based on the signal-to-noise ratio corresponding to each of the integrated imaging results of the target area and the integration time corresponding to each of the integrated imaging results of the target area, a signal-to-noise ratio variation curve of the signal-to-noise ratio of the target area with respect to the integration time is constructed, and the integration time corresponding to the maximum value in the signal-to-noise ratio variation curve is used as the first integration time.

[0041] In one embodiment, the determining module is further used to:

[0042] According to the pixel points in the background area and whose brightness reaches saturation in each of the integral imaging results, respectively determining the saturation integration time when the brightness of each of the pixel points in the background area reaches saturation;

[0043] According to the overexposure suppression strategy and the saturation integration time, the second integration time corresponding to each of the pixel points is determined respectively.

[0044] In one embodiment, the reconstruction module is further used for:

[0045] Determine, according to the pulse sampling time and the preset sampling duration, each imaging moment corresponding to the particle region within the preset sampling duration;

[0046] Respectively determining a first integral imaging result corresponding to the integral imaging result of the background area at each of the imaging moments, and respectively determining a second integral imaging result corresponding to the integral imaging result of the target area at each of the imaging moments;

[0047] Based on the first integral imaging result and the second integral imaging result, target particle images corresponding to each imaging moment are generated respectively.

[0048] In one embodiment, the above device further comprises:

[0049] A denoising module, configured to perform denoising processing on each target particle image based on the pulse emission frequency corresponding to each pixel point in the original pulse image data, the pixel difference between each two adjacent target particle images, and at least one of a first target filter and a second target filter, to obtain a denoised target particle image;

[0050] The first target filter is constructed based on the target shape characteristics of the particles, and the second target filter is constructed based on the target motion characteristics of the particles.

[0051] In one embodiment, the denoising module is further used for:

[0052] A difference processing is performed on any two adjacent frames of target particle images. When the pixel difference corresponding to any pixel point in the two frames of target particle images is greater than a first preset threshold and less than a second preset threshold, the pixel point is determined as a noise pixel point, and denoising is performed on each of the noise pixel points in the two frames of target particle images.

[0053] In one embodiment, the target shape feature of the particle includes an area feature and an intensity feature, wherein the area feature is that the number of pixel points corresponding to a single particle in the particle image is greater than 1, and the intensity feature is that among all the pixel points corresponding to a single particle in the particle image, the intensity of the central pixel point is greater than the intensity of the edge pixel point;

[0054] The target motion feature of the particle is that in the original pulse image data, the change values ​​of the pulse emission frequencies of a plurality of adjacent pixel points have a correlation relationship.

[0055] In a third aspect, the present application further provides a computer device, wherein the computer device comprises a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, any one of the above methods is implemented.

[0056] In a fourth aspect, the present application further provides a computer-readable storage medium, wherein a computer program is stored on the computer-readable storage medium, and when the computer program is executed by a processor, any of the above methods is implemented.

[0057] In a fifth aspect, the present application further provides a computer program product, wherein the computer program product comprises a computer program, and when the computer program is executed by a processor, any of the above methods is implemented.

[0058] The above-mentioned particle image reconstruction method, device, computer equipment and storage medium image the original pulse image data according to different integration times, identify the target area containing particles and the background area not containing particles in the original pulse image data according to the integral imaging results, and then determine the first integration time used when performing integral imaging on the target area according to the signal-to-noise ratio corresponding to different integration times of the target area, and determine the second integration time corresponding to the background area according to the brightness of the background area in different integral imaging results, and then use the determined integration time to perform integral imaging on each area, so as to obtain the target particle image corresponding to the particle area. The embodiment of the present application determines different integration times for different areas, and uses a better integration time for imaging for each area. Therefore, when processing images with large brightness differences in different areas, it is also possible to take into account both bright and dark areas for image reconstruction, thereby improving the reconstruction accuracy of the particle image. BRIEF DESCRIPTION OF THE DRAWINGS

[0059] Figure 1 is a schematic flow chart of a particle image reconstruction method in one embodiment;

[0060] Figure 2 is a flow chart of step 102 in one embodiment;

[0061] Figure 3 A schematic diagram of iteratively determining a target area in one embodiment;

[0062] Figure 4 A schematic diagram of a flow chart of determining a first integration time in step 104 in one embodiment;

[0063] Figure 5 is a schematic diagram of a signal-to-noise ratio variation curve in one embodiment;

[0064] Figure 6 A schematic diagram of a flow chart of determining the second integration time in step 104 in one embodiment;

[0065] Figure 7 is a flow chart of step 106 in one embodiment;

[0066] Figure 8 is a schematic diagram of reconstructing a target particle image in one embodiment;

[0067] Fig. 9 A schematic diagram of denoising based on pulse emission frequency in one embodiment;

[0068] Fig.10 A schematic diagram of the appearance characteristics of a particle target in one embodiment;

[0069] Fig.11 is a schematic diagram of a second target filter in one embodiment;

[0070] Fig.12 is a structural block diagram of a particle image reconstruction device in one embodiment;

[0071] Fig.13 FIG. 4 is a diagram showing the internal structure of a computer device in one embodiment. DETAILED DESCRIPTION

[0072] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0073] In one embodiment, Figure 1As shown, a particle image reconstruction method is provided. This embodiment uses the method applied to a server as an example for illustration. It is understandable that the method can also be applied to a terminal, and can also be applied to a system including a terminal and a server, and is implemented through the interaction between the terminal and the server. In this embodiment, the method includes the following steps:

[0074] Step 102, collect raw pulse image data for the particle area within a preset sampling time, perform integral imaging processing on the raw pulse image data multiple times using different integration times, and determine the target area including particles and the background area excluding particles in the raw pulse image data based on the obtained integral imaging results.

[0075] In an embodiment of the present application, continuous raw pulse image data is collected by a pulse high-speed camera within a preset sampling time, and the raw pulse image data is integrated using different integration times to obtain imaging results corresponding to different integration times. The integration time refers to the length of time for accumulating raw pulse image data when imaging a frame of image, and the specific different integration times used to integrate the raw pulse image data can be determined according to actual needs. For example, starting from the shortest integration time of the pulse high-speed camera, the integration time can be gradually increased according to a preset integration time increase strategy (for example, increasing the integration time by a certain step length each time, using a certain multiple of the previous integration time each time, etc.) to integrate the raw pulse image data until all pixels in the imaging result are saturated when a certain integration time is used, thereby obtaining integral imaging results corresponding to multiple different integration times.

[0076] After obtaining the imaging results corresponding to different integration times, the target area including particles and the background area excluding particles in the original pulse image data can be divided according to each integral imaging result. For example, any particle recognition algorithm (such as an edge detection algorithm or a trained neural network model) can be used to identify the parts whose morphology conforms to the particle contour morphology from each integral imaging result, and then the particle area containing particles in the integral imaging result can be determined based on these parts, and the part corresponding to the particle area in the original pulse image data can be used as the target area. After determining each target area, the part of the original pulse image data other than the target area can be used as the background area.

[0077] It should be noted that a particle area can contain multiple particles or only one particle. For example, after detecting each particle in the integral imaging result, an area where the particle density is greater than a preset threshold can be used as a particle area, or an area where a particle is located can be used as a particle area. Since different first integral times are required for integral imaging of different target areas in the future, and the single pixel residence time of different particles is usually different, when the particle area contains fewer particles, the first integral time determined for the target area corresponding to the particle area is more accurate, and the imaging accuracy is higher, but it will also bring greater computing power consumption. Those skilled in the art can determine the particle area division strategy according to actual needs.

[0078] Step 104, determining the signal-to-noise ratio of each target area corresponding to different integration times, determining the first integration time corresponding to each target area according to each signal-to-noise ratio, and determining the second integration time corresponding to the background area according to the brightness of the background area in each integrated imaging result.

[0079] In an embodiment of the present application, after acquiring each target area, the trend of the signal-to-noise ratio of each target area changing with the integration time can be determined, and the first integration time with better imaging effect when performing integral imaging on the target area can be determined based on the trend. Here, different integration times can be used to perform integral imaging on each target area again. For example, more integral imaging times can be used for integral imaging compared to the previous step to obtain more integral imaging results of the target area, so as to obtain a more accurate trend of the signal-to-noise ratio of the target area changing with the integration time. Alternatively, the corresponding part of the target area can be determined from each integral imaging result obtained in the previous step, and the trend of the signal-to-noise ratio of the target area changing with the integration time can be determined based on the signal-to-noise ratio of these parts.

[0080] The signal-to-noise ratio of the target area can be determined based on the pixel value intensity of the particle-containing part and the pixel value intensity of the particle-free part in each integral imaging result. After determining the signal-to-noise ratio of the target area corresponding to different integration times, the integration time corresponding to the highest signal-to-noise ratio can be used as the first integration time, or a relationship function between the signal-to-noise ratio and the integration time can be fitted, and the integration time corresponding to the maximum value of the relationship function is taken as the first integration time, which is not specifically limited in the embodiments of the present application.

[0081] For the background area, in order to prevent the background area from being overexposed during the integral imaging process and affecting the imaging effect of the particles, an integration time that can make the background area have a lower brightness can be selected as the second integration time. Exemplarily, the brightness of the background area in each integral imaging result can be determined, and the integration time corresponding to the integral imaging result with the lowest brightness can be selected as the second integration time.

[0082] Step 106, performing integral imaging processing on each target area according to the first integral time, and performing integral imaging processing on the background area according to the second integral time, and reconstructing multiple frames of target particle images corresponding to the particle area based on the integral imaging results of each target area and the background area.

[0083] In the embodiment of the present application, the first integration time determined for each target area is used to perform integration imaging processing on each target area, and the second integration time determined for the background area is used to perform integration imaging processing on the background area. After the integration imaging results of each target area and the integration imaging results of the background area are aligned in the time domain, a multi-frame particle image reconstructed for the particle area can be obtained.

[0084] The particle image reconstruction method provided in the embodiment of the present application performs imaging on the original pulse image data according to different integration times, identifies the target area containing particles and the background area not containing particles in the original pulse image data according to the integration imaging results, and then determines the first integration time used when performing integration imaging on the target area according to the signal-to-noise ratio corresponding to the different integration times of the target area, and determines the second integration time corresponding to the background area according to the brightness of the background area in different integration imaging results, and then uses the determined integration time to perform integration imaging on each area, and obtains the target particle image corresponding to the particle area. The embodiment of the present application determines different integration times for different areas, and uses a better integration time for imaging for each area. Therefore, when processing images with large brightness differences in different areas, it is also possible to take into account both bright and dark areas for image reconstruction, thereby improving the reconstruction accuracy of the particle image.

[0085] In one embodiment, Figure 2 As shown, in step 102, the original pulse image data is subjected to integral imaging processing for multiple times using different integral times, and the target area including particles and the background area not including particles in the original pulse image data are determined according to each integral imaging result obtained, including:

[0086] Step 202, using the original pulse image data as target pulse image data, performing integral imaging processing on the target pulse image data using a target integration time to obtain an integral imaging result, determining a particle region including the particle from the integral imaging result, and using a portion of the original pulse image data corresponding to the particle region as the target region;

[0087] Step 204, taking the portion of the original pulse image data except the target areas as the target pulse image data, increasing the target integration time, and jumping to the step of performing integration imaging processing on the target pulse image data using the target integration time to obtain the integration imaging result, until all the pixels in the integration imaging result are saturated;

[0088] Step 206: Use the portion of the original pulse image data corresponding to the current target pulse image data as the background area.

[0089] In the embodiment of the present application, after each integral imaging process, the target area containing particles is identified from the integral imaging result, and the integral time of the non-target area in the original pulse image data is gradually increased, and all target areas are determined by iterative integral imaging. The schematic diagram of the process and the various target areas determined can be seen in Figure 3 shown. Figure 3 The area selected after each iteration is the target area.

[0090] The initially determined target integration time can be a shorter time. Using a shorter integration time for integration imaging can avoid motion blur and regional overexposure of high-speed particles in the imaging results, which affects particle identification. After integration imaging is performed according to the target integration time, based on the particle recognition algorithm, particles are identified and particle regions are divided from each integral imaging result obtained. The method of dividing the particle region can be found in the description of the previous embodiment, and the embodiments of this application will not be repeated.

[0091] After the part of the original pulse image data corresponding to the particle area is used as the target area, the part other than the target area can be used as the target pulse image data in the next iteration to avoid interference of the energy-strong particles determined in the previous round on the imaging results in the next iteration. At the same time, the target integration time can be increased according to the preset integration time increase strategy, and the target pulse image data can be processed by integration imaging until all pixels in the integration imaging result are saturated. At this time, it can be considered that the weakest energy particle that the pulse high-speed camera can theoretically detect has been detected, and the remaining area where the particle is not detected (that is, the area corresponding to the current target pulse image data) is the background area that does not contain particles.

[0092] In one embodiment, Figure 4 As shown, in step 104, the signal-to-noise ratios of each target area corresponding to different integration times are determined, and the first integration time corresponding to each target area is determined according to each signal-to-noise ratio, including:

[0093] Step 402, performing integral imaging processing on each target area using different integral times, obtaining a plurality of integral imaging results of each target area, and determining the signal-to-noise ratio of each integral imaging result;

[0094] Step 404, for any target area, based on the signal-to-noise ratio corresponding to each integral imaging result of the target area and the integration time corresponding to each integral imaging result of the target area, a signal-to-noise ratio variation curve of the signal-to-noise ratio of the target area with respect to the integration time is constructed, and the integration time corresponding to the maximum value in the signal-to-noise ratio variation curve is used as the first integration time.

[0095] In the embodiment of the present application, after each target area is determined, different integration times may be used again to perform integration imaging processing on each target area. The number of integration times used in this process may be greater than the number of integration times used in step 102, and the difference between each integration time used may also be shorter than that in step 102, so that the relationship between the determined signal-to-noise ratio and the integration time is more accurate.

[0096] Since the particles are in high-speed motion and the single pixel residence time is limited, when the integration time is less than the single pixel residence time of the particles, the signal-to-noise ratio of the target area corresponding to the particles is proportional to the integration time. When the integration time exceeds the single pixel residence time of the particles, the particle displacement produces imaging blur and tailing, so the signal-to-noise ratio will be inversely proportional to the integration time. The theoretical formula for the relationship between the signal-to-noise ratio and the integration time can be found in formula (1):

[0097] Formula (I)

[0098] in, is the signal-to-noise ratio under integration time t, is the particle intensity per unit time, is the background intensity per unit time, is the single pixel residence time of the particle.

[0099] Therefore, it can be determined that when the integration time is the single pixel residence time of the particle, the signal-to-noise ratio of the target area is the largest and the imaging effect of the target area is the best. After determining the signal-to-noise ratio of the target area for different integration times, the signal-to-noise ratio change curve with respect to the integration time can be obtained by function fitting or interpolation, and then the integration time corresponding to the maximum value (i.e., the maximum point of the signal-to-noise ratio) in the signal-to-noise ratio change curve is used as the first integration time.

[0100] Reference Figure 5 The figure shows the schematic diagram of the signal-to-noise ratio change curve. Figure 5It can be determined that the integration time when the signal-to-noise ratio change curve of the target area where particle 1 is located reaches the maximum value is 1ms, the integration time when the signal-to-noise ratio change curve of the target area where particle 2 is located reaches the maximum value is 3.5ms, and the integration time when the signal-to-noise ratio change curve of the target area where particle 3 is located reaches the maximum value is 6ms. Therefore, it can be determined that the first integration time of the target area where particle 1 is located is 1ms, the first integration time of the target area where particle 2 is located is 3.5ms, and the first integration time of the target area where particle 3 is located is 6ms.

[0101] The particle image reconstruction method provided in the embodiment of the present application constructs a signal-to-noise ratio variation curve in which the signal-to-noise ratio of the target area varies with the integration time, and selects the integration time corresponding to the maximum value in the signal-to-noise ratio variation curve as the first integration time. Therefore, for each target area, the first integration time that can maximize its signal-to-noise ratio can be used to perform integral imaging processing on it, thereby improving the accuracy of the integral imaging result.

[0102] In one embodiment, Figure 6 As shown, in step 104, according to the brightness of the background area in each integral imaging result, determining the second integration time corresponding to the background area includes:

[0103] Step 602, according to the pixel points in the background area and whose brightness is saturated in each integral imaging result, respectively determine the saturation integration time when the brightness of each pixel point in the background area is saturated;

[0104] Step 604 : Determine the second integration time corresponding to each pixel point according to the overexposure suppression strategy and the saturation integration time.

[0105] In the embodiment of the present application, since the brightness of each pixel may be inconsistent when the same second integration time is used for the background area for integration imaging, there will be a problem of uneven background area. Therefore, in order to make the brightness of the background area in the final particle image more uniform, a saturation integration time that can make the pixel reach saturation can be determined for each pixel in the background area, and then the saturation integration time is processed based on the overexposure suppression strategy to obtain the second integration time, so that each pixel can be processed for integration imaging according to a different second integration time, and finally an integration imaging result with similar brightness is obtained for each pixel.

[0106] The original pulse image data can be subjected to integral imaging processing by using different integral times, and after the background area in the original pulse image data is determined, the part corresponding to the background area can be determined from each integral imaging result, and it can be determined that each pixel in the background area reaches saturation in the integral imaging result corresponding to which integral time, so as to obtain the saturated integral time of each pixel. Alternatively, a relationship curve between the brightness of each pixel and the integral time can be obtained by fitting according to the brightness of each pixel in each integral imaging result, and the integral time corresponding to the brightness of the pixel in the relationship curve reaching the saturation value is used as the saturated integral time, and the embodiment of the present application does not specifically limit this.

[0107] After processing the saturation integration time based on the overexposure suppression strategy, a second integration time can be obtained. The embodiment of the present application does not specifically limit the overexposure suppression strategy, as long as after the pixel points in the background area are subjected to integral imaging processing at the second integration time obtained according to the overexposure suppression strategy, the brightness difference of each pixel point in the obtained integral imaging result is within a certain range, and the brightness of each pixel point is lower than a preset brightness threshold. Exemplarily, the overexposure suppression strategy may be to multiply the saturation integration time by an overexposure suppression coefficient (such as 0.2), or the overexposure suppression strategy may be to subtract a preset value from the saturation integration time, etc. The embodiment of the present application does not specifically limit this.

[0108] Taking the overexposure suppression strategy of multiplying the saturation integration time by an overexposure suppression coefficient as an example, for any pixel point in the original pulse image data, the integration time formula of the pixel point is shown in formula (2):

[0109] Formula (II)

[0110] in, For location The pixel integration time is When the pixel belongs to the target area, the first integration time of the target area to which it belongs, When the pixel belongs to the background area, the saturation integration time determined for the pixel. is the overexposure suppression coefficient.

[0111] The particle image reconstruction method provided in the embodiment of the present application determines the saturation integration time when each pixel in the background area reaches saturation, processes the saturation integration time according to the overexposure suppression strategy, obtains the second integration time corresponding to each pixel, and uses different second integration times to perform integration imaging processing on each pixel in the background area. Therefore, the optimal integration time that can suppress overexposure can be determined for each pixel in the background area, avoiding the phenomenon of partial overexposure in the background area, and improving the imaging accuracy of the particle image.

[0112] In one embodiment, Figure 7 As shown, in step 106, based on the integral imaging results of each target area and the background area, a multi-frame target particle image corresponding to the particle area is reconstructed, including:

[0113] Step 702, determining each imaging moment corresponding to the particle region within the preset sampling time length according to the pulse sampling time and the preset sampling time length;

[0114] Step 704, respectively determining the first integral imaging result corresponding to the integral imaging result of the background area at each imaging moment, and respectively determining the second integral imaging result corresponding to the integral imaging result of each target area at each imaging moment;

[0115] Step 706: Generate target particle images corresponding to each imaging moment based on the first integral imaging result and the second integral imaging result.

[0116] In the embodiment of the present application, the pulse sampling time is the shortest time required for the pulse high-speed camera to sample one frame of raw pulse image data, so the integration time is a multiple of the pulse sampling time of the pulse high-speed camera, and the pulse sampling time can be used as the basic time unit for particle image reconstruction. Theoretically, the preset sampling time divided by the pulse sampling time can be sampled in the preset sampling time, so the preset sampling time divided by the pulse sampling time can also be used to obtain the target particle image.

[0117] The imaging moment of each frame of the target particle image in the preset sampling time can be determined according to the pulse sampling time, and then the first and second integral imaging results corresponding to the imaging moment can be determined from the integral imaging results of each target area and background area, and these integral imaging results can be combined into the target particle image corresponding to the imaging moment.

[0118] The pulse sampling time is T s , the integration time of a target area is T z For example, the target area passes through n=T z / T s Further, referring to the description in the above embodiment, since the integration time of the target area is the single pixel residence time of the particles in the target area, it means that the particles will not move within an integration time. Therefore, if the target area is T m To T m+n The integral imaging results were obtained during this period. , then T m To T m+n The first integral imaging results corresponding to the n imaging moments can all be .

[0119] By similar methods, the first integral imaging result corresponding to the integral imaging results of other target areas and the second integral imaging result corresponding to the integral imaging results of the background area at each imaging moment can be determined. After obtaining the first integral imaging result corresponding to all target areas and the second integral imaging result corresponding to the background area at any imaging moment, these integral imaging results can be combined to obtain the target particle image.

[0120] See also Figure 8 The diagram shown is a schematic diagram of the above process. Figure 8 The imaging time is from t0 to t5. The original pulse image data collected from t0 to t5 can be found in Figure 8 The upper half of the image is obtained by analyzing the signal-to-noise ratio of the above embodiment. For all target regions where particles in the top region have moved (hereinafter referred to as the top target region), the first integration time is 3T. s For all target areas that particles in the middle area have moved to (hereinafter referred to as the middle target area), the first integration time is 2T s , for all target areas that particles in the lower area have moved to (hereinafter referred to as the lower target area), the first integration time is T s .

[0121] Using the first integration time to perform integral imaging on each target area, the integral imaging result of the first lower target area can be obtained at t0, the integral imaging results of the second lower target area and the first middle target area can be obtained at t1, the integral imaging results of the third lower target area and the first top target area can be obtained at t2, and so on. At the same time, since the particles in the middle area have been in the first middle target area from t0 to t1, the position of the particles in the middle area reflected by the integral imaging result of the first middle target area at t1 is also the position of the particles in the middle area at t0. Therefore, the integral imaging result of the first middle target area obtained at t1 can be copied to t0. Similarly, similar operations can be performed for other middle target areas and other top target areas. The target particle image finally obtained at each imaging moment is as follows: Figure 8 Shown in the lower part.

[0122] The particle image reconstruction method provided in the embodiment of the present application performs time frame alignment on the integral imaging results of each target area after obtaining the integral imaging results, and can generate a target particle image with higher clarity for each imaging moment in a preset sampling duration, thereby improving the generation accuracy of the target particle image.

[0123] In one embodiment, the method further includes:

[0124] Based on the pixel difference between each two adjacent target particle images and at least one of the first target filter and the second target filter, each target particle image is subjected to denoising processing to obtain a denoised target particle image;

[0125] The first target filter is constructed based on the target shape characteristics of the particles, and the second target filter is constructed based on the target motion characteristics of the particles.

[0126] In the embodiment of the present application, since the pulse emission sensitivity of the pixels in the pulse high-speed camera is inevitably inconsistent, and the camera circuit generates dark current during actual operation, spatial and temporal noise may appear in the reconstructed target particle image, resulting in a reduced signal-to-noise ratio, affecting the subsequent particle velocity PIV effect. Therefore, after the target particle image is reconstructed, the target particle image can also be denoised.

[0127] The denoising method may adopt pulse emission frequency analysis, pixel difference between each two adjacent target particle images (i.e., frame difference method) or filter, wherein the filter may be constructed based on the target shape characteristics and target motion characteristics generally possessed by the particles in the particle images taken for the particles. The target shape characteristics may include the intensity characteristics of the pixel points containing the particles, the shape characteristics of the area where the particles are located, etc. The target motion characteristics may include the pulse emission frequency change law of each adjacent pixel point when the particle moves, etc.

[0128] The principle of denoising based on pulse emission frequency is that the brightness of particles in the particle area is generally within a fixed range. Therefore, when using a pulse high-speed camera to shoot the particle area, the pulse emission frequency corresponding to each pixel should also be within a fixed range. This fixed range can be obtained by statistical methods. Fig. 9 As shown in FIG. 1 , a histogram of the number of pixels having a certain pulse emission frequency is obtained after the pulse emission frequency corresponding to each pixel is counted. The abnormal values ​​in the histogram can be determined by the relevant algorithm in statistics, and then the pixels corresponding to these abnormal values ​​can be determined as pixels containing time domain noise, and these pixels in each target particle image can be denoised by any image denoising algorithm.

[0129] In one embodiment, denoising each target particle image based on the pixel difference between each two adjacent initial particle images includes:

[0130] A difference process is performed on any two adjacent target particle images. When the pixel difference corresponding to any pixel point in the two target particle images is greater than a first preset threshold and less than a second preset threshold, the pixel point is determined as a noise pixel point, and denoising is performed on each noise pixel point in the two target particle images.

[0131] In the embodiment of the present application, the frame difference method is used to denoise the target particle image. Since the shooting position of the pulse high-speed camera is fixed in the particle velocity PIV scenario, the background area is in a stationary state during the shooting process, which is reflected in the image as the pixel values ​​of the background area in different target particle images are generally not much different. Only the movement of the particles and the time domain noise generated by the accumulation of dark current will cause the target particle image to produce pixel value changes. At the same time, compared with the pixel value changes caused by time domain noise, the pixel value changes caused by the movement of particles are generally more drastic. Therefore, based on this feature, the first preset threshold and the second preset threshold can be statistically calculated by simulation experiments and the like. If the pixel difference corresponding to a certain pixel point is less than or equal to the first preset threshold value after the difference is made between the two frames of target particle images, it can be considered that the pixel point belongs to the background area. If the pixel difference corresponding to a certain pixel point is greater than or equal to the second preset threshold value, it can be considered that the pixel point belongs to the particle motion area. When the pixel difference corresponding to the pixel point is greater than the first preset threshold value and less than the second preset threshold value, it can be considered that the pixel value change of the pixel point is caused by noise. At this time, any image denoising algorithm can be used to denoise the pixel points in the two frames of target particle images respectively.

[0132] In one embodiment, the target shape feature of the particle includes an area feature and an intensity feature. The area feature is that the number of pixel points corresponding to a single particle in the particle image is greater than 1, and the intensity feature is that among all the pixel points corresponding to a single particle in the particle image, the intensity of the central pixel point is greater than the intensity of the edge pixel points.

[0133] The target motion feature of the particles is that in the original pulse image data, the change values ​​of the pulse emission frequencies of a plurality of adjacent pixel points have a correlation relationship.

[0134] In the embodiment of the present application, a particle generally occupies multiple pixels in the particle image, and the array of pixels occupied presents a dot-like appearance feature (the intensity of the central pixel is the largest, and the intensity gradually decreases outward), while noise usually occupies a single pixel, or the array of pixels occupied has an irregular shape, such as Fig.10 As shown. Therefore, based on this feature, the target shape feature of the particle can be determined as the number of pixels corresponding to a single particle in the particle image is greater than 1, and the intensity of the central pixel is greater than the intensity of the edge pixel in all the pixels corresponding to a single particle in the particle image. Based on the above target shape features, a high-pass filter can be designed as the first target filter, and each target particle image is convolved to obtain a denoised image. The specific formula of the high-pass filter is shown in formula (III):

[0135] Formula (III)

[0136] in, is the target particle image, is the convolution kernel of the high-pass filter, The target particle image at the coordinate value The value of is the convolution kernel relative to Deviation By setting the threshold value of the high-pass filter, we can get a target that meets the shape characteristics of the particle target, thereby removing some of the time domain noise.

[0137] The principle of the target motion characteristics of particles is that since the particles are in a high-speed motion state, there is a certain correlation between the pulse emission frequencies of adjacent pixels in the original pulse image data. Specifically, when the pulse emission frequency characteristics of the current pixel change, the pulse emission frequency characteristics of the adjacent pixels change in a correlated manner, and the two parts of the change value are in a corresponding relationship. Therefore, based on the above-mentioned particle motion characteristics, the following can be designed: Fig.11 The multi-scale time domain filter shown is used as the second target filter. A time window is superimposed on each current frame target particle image. The target number of pixel points whose pulse emission frequency characteristics are detected in a time window in each area is calculated through convolution, and the target number in the two scale windows and the target number in the window are compared with the set prior threshold size, so as to screen and filter out the time domain noise that does not conform to the motion characteristics, and finally restore and reconstruct a clear high signal-to-noise ratio image.

[0138] Compared with the conventional high-speed camera particle image velocimetry (PIV) method and existing pulse image reconstruction technology, the above-mentioned particle image reconstruction method has the following significant advantages:

[0139] Pulse image reconstruction under wide dynamic range: It can reconstruct clear images for particle image velocimetry (PIV) in high dynamic range imaging scenarios such as combustion flow fields or strong backlight flow fields, overcoming the problems of traditional cameras overexposure in bright areas and underexposure in low-bright areas, as well as the inability of traditional pulse imaging technology to take into account both bright and dark areas and blurred tailing of high-speed particles.

[0140] Denoising method based on particle spatiotemporal characteristics: In the particle image velocimetry (PIV) scenario, a denoising method is proposed that combines the characteristics of particles in the spatial domain (intensity and shape characteristics) and the temporal domain (motion characteristics). This method effectively suppresses and removes the spatial noise caused by the inconsistent pulse emission sensitivity of each pixel and the temporal noise caused by the accumulation of dark current, thereby achieving high signal-to-noise ratio imaging.

[0141] Image reconstruction algorithm based on regional adaptive integration: An image reconstruction algorithm based on regional adaptive integration is proposed. It is specially designed and optimized for the characteristics of pulse image data and moving particles. The target area and background area containing particles are iteratively and finely divided based on the independent integration characteristics of each pixel of the pulse image data. According to the signal-to-noise ratio change curve of the target area, the pixel residence time of each particle is determined to obtain the optimal integration time that takes into account both bright and dark areas. At the same time, overexposure suppression is performed according to the saturated integration time of the background area to prevent image overexposure, thereby realizing the conversion of the original pulse data of the pulse high-speed camera into a high-dynamic particle image.

[0142] It should be understood that, although the various steps in the flowcharts involved in the above-mentioned embodiments are displayed in sequence according to the indication of the arrows, these steps are not necessarily executed in sequence according to the order indicated by the arrows. Unless there is a clear explanation in this article, the execution of these steps does not have a strict order restriction, and these steps can be executed in other orders. Moreover, at least a part of the steps in the flowcharts involved in the above-mentioned embodiments can include multiple steps or multiple stages, and these steps or stages are not necessarily executed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily to be carried out in sequence, but can be executed in turn or alternately with other steps or at least a part of the steps or stages in other steps.

[0143] Based on the same inventive concept, the embodiment of the present application also provides a particle image reconstruction device for implementing the particle image reconstruction method involved above. The implementation solution provided by the device to solve the problem is similar to the implementation solution recorded in the above method, so the specific limitations in one or more particle image reconstruction device embodiments provided below can refer to the limitations of the particle image reconstruction method above, and will not be repeated here.

[0144] In one embodiment, Fig.12 As shown, a particle image reconstruction device 1200 is provided, comprising: an integration module 1202, a determination module 1204, and a reconstruction module 1206, wherein:

[0145] An integration module 1202 is used to collect raw pulse image data for a particle region within a preset sampling time, perform integral imaging processing on the raw pulse image data using different integral times for multiple times, and determine a target region including particles and a background region not including particles in the raw pulse image data according to each integral imaging result obtained;

[0146] A determination module 1204 is used to determine the signal-to-noise ratio of each target area corresponding to different integration times, determine the first integration time corresponding to each target area according to each signal-to-noise ratio, and determine the second integration time corresponding to the background area according to the brightness of the background area in each integral imaging result;

[0147] The reconstruction module 1206 is used to perform integral imaging processing on each of the target areas according to the first integration time, and to perform integral imaging processing on the background area according to the second integration time, and to reconstruct a multi-frame target particle image corresponding to the particle area based on the integral imaging results of each of the target areas and the background area.

[0148] The particle image reconstruction device provided in the embodiment of the present application images the original pulse image data according to different integration times, identifies the target area containing particles and the background area not containing particles in the original pulse image data according to the integration imaging results, and then determines the first integration time used when performing integration imaging on the target area according to the signal-to-noise ratio corresponding to the different integration times of the target area, and determines the second integration time corresponding to the background area according to the brightness of the background area in different integration imaging results, and then uses the determined integration time to perform integration imaging for each area, and obtains the target particle image corresponding to the particle area. The embodiment of the present application determines different integration times for different areas, and uses a better integration time for imaging for each area. Therefore, when processing images with large brightness differences in different areas, it is also possible to take into account both bright and dark areas for image reconstruction, thereby improving the reconstruction accuracy of the particle image.

[0149] In one embodiment, the integration module 1202 is further used for:

[0150] The original pulse image data is used as target pulse image data, and the target pulse image data is subjected to integral imaging processing using a target integration time to obtain an integral imaging result, and a particle region including particles is determined from the integral imaging result, and a portion of the original pulse image data corresponding to the particle region is used as a target region;

[0151] The part of the original pulse image data except the target areas is used as the target pulse image data, and the target integration time is increased, and the process jumps to the step of performing integral imaging processing on the target pulse image data using the target integration time to obtain an integral imaging result, until all the pixels in the integral imaging result are saturated;

[0152] The portion of the original pulse image data corresponding to the current target pulse image data is used as the background area.

[0153] In one embodiment, the determining module 1204 is further configured to:

[0154] Performing integral imaging processing on each of the target areas using different integral times to obtain a plurality of integral imaging results for each of the target areas, and determining a signal-to-noise ratio of each of the integral imaging results;

[0155] For any of the target areas, based on the signal-to-noise ratio corresponding to each of the integrated imaging results of the target area and the integration time corresponding to each of the integrated imaging results of the target area, a signal-to-noise ratio variation curve of the signal-to-noise ratio of the target area with respect to the integration time is constructed, and the integration time corresponding to the maximum value in the signal-to-noise ratio variation curve is used as the first integration time.

[0156] In one embodiment, the determining module 1204 is further configured to:

[0157] According to the pixel points in the background area and whose brightness reaches saturation in each of the integral imaging results, respectively determining the saturation integration time when the brightness of each of the pixel points in the background area reaches saturation;

[0158] According to the overexposure suppression strategy and the saturation integration time, the second integration time corresponding to each of the pixel points is determined respectively.

[0159] In one embodiment, the reconstruction module 1206 is further configured to:

[0160] Determine, according to the pulse sampling time and the preset sampling duration, each imaging moment corresponding to the particle region within the preset sampling duration;

[0161] Respectively determining a first integral imaging result corresponding to the integral imaging result of the background area at each of the imaging moments, and respectively determining a second integral imaging result corresponding to the integral imaging result of the target area at each of the imaging moments;

[0162] Based on the first integral imaging result and the second integral imaging result, target particle images corresponding to each imaging moment are generated respectively.

[0163] In one embodiment, the above device further comprises:

[0164] A denoising module, configured to perform denoising processing on each target particle image based on the pulse emission frequency corresponding to each pixel point in the original pulse image data, the pixel difference between each two adjacent target particle images, and at least one of a first target filter and a second target filter, to obtain a denoised target particle image;

[0165] The first target filter is constructed based on the target shape characteristics of the particles, and the second target filter is constructed based on the target motion characteristics of the particles.

[0166] In one embodiment, the denoising module is further used for:

[0167] A difference processing is performed on any two adjacent frames of target particle images. When the pixel difference corresponding to any pixel point in the two frames of target particle images is greater than a first preset threshold and less than a second preset threshold, the pixel point is determined as a noise pixel point, and denoising is performed on each of the noise pixel points in the two frames of target particle images.

[0168] In one embodiment, the target shape feature of the particle includes an area feature and an intensity feature, wherein the area feature is that the number of pixel points corresponding to a single particle in the particle image is greater than 1, and the intensity feature is that among all the pixel points corresponding to a single particle in the particle image, the intensity of the central pixel point is greater than the intensity of the edge pixel point;

[0169] The target motion feature of the particle is that in the original pulse image data, the change values ​​of the pulse emission frequencies of a plurality of adjacent pixel points have a correlation relationship.

[0170] Each module in the above device can be implemented in whole or in part by software, hardware or a combination thereof. Each module can be embedded in or independent of a processor in a computer device in the form of hardware, or can be stored in a memory in a computer device in the form of software, so that the processor can call and execute operations corresponding to each module above.

[0171] In one embodiment, a computer device is provided. The computer device may be a server, and its internal structure diagram may be as follows: Fig.13 As shown. The computer device includes a processor, a memory and a network interface connected through a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The network interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a particle image reconstruction method is implemented.

[0172] Those skilled in the art will understand that Fig.13The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0173] In one embodiment, a computer device is provided, including a memory and a processor, wherein a computer program is stored in the memory, and the processor implements the steps in the above-mentioned method embodiments when executing the computer program.

[0174] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the steps in the above-mentioned method embodiments are implemented.

[0175] In one embodiment, a computer program product is provided, including a computer program, which implements the steps in the above method embodiments when executed by a processor.

[0176] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties.

[0177] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to the memory, database or other medium used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetoresistive random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. As an illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. Non-relational databases may include distributed databases based on blockchains, etc., but are not limited to this. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., but are not limited to this.

[0178] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0179] The above-described embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the present application. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the attached claims.

Claims

1. A particle image reconstruction method, characterized in that: The method comprises: Collecting raw pulse image data for a particle region within a preset sampling time, performing integral imaging processing on the raw pulse image data for multiple times using different integral times, and determining a target region including particles and a background region excluding particles in the raw pulse image data according to each integral imaging result obtained; Determine the signal-to-noise ratio of each target area corresponding to different integration times, determine the first integration time corresponding to each target area according to each signal-to-noise ratio, and determine the second integration time corresponding to the background area according to the brightness of the background area in each integral imaging result; Integral imaging processing is performed on each of the target areas according to the first integration time, and integral imaging processing is performed on the background area according to the second integration time. Based on the integral imaging results of each of the target areas and the background area, a multi-frame target particle image corresponding to the particle area is reconstructed.

2. The method according to claim 1, characterized in that The step of performing integral imaging processing on the raw pulse image data for multiple times using different integral times, and determining a target area including particles and a background area not including particles in the raw pulse image data according to each integral imaging result obtained, comprises: The original pulse image data is used as target pulse image data, and the target pulse image data is subjected to integral imaging processing using a target integration time to obtain an integral imaging result, and a particle region including particles is determined from the integral imaging result, and a portion of the original pulse image data corresponding to the particle region is used as a target region; The part of the original pulse image data except the target areas is used as the target pulse image data, and the target integration time is increased, and the process jumps to the step of performing integral imaging processing on the target pulse image data using the target integration time to obtain an integral imaging result, until all the pixels in the integral imaging result are saturated; The portion of the original pulse image data corresponding to the current target pulse image data is used as the background area.

3. The method according to claim 1, characterized in that The step of determining the signal-to-noise ratios of the target areas corresponding to different integration times, and determining the first integration time corresponding to the target areas according to the signal-to-noise ratios, comprises: Performing integral imaging processing on each of the target areas using different integral times to obtain a plurality of integral imaging results for each of the target areas, and determining a signal-to-noise ratio of each of the integral imaging results; For any of the target areas, based on the signal-to-noise ratio corresponding to each of the integrated imaging results of the target area and the integration time corresponding to each of the integrated imaging results of the target area, a signal-to-noise ratio variation curve of the signal-to-noise ratio of the target area with respect to the integration time is constructed, and the integration time corresponding to the maximum value in the signal-to-noise ratio variation curve is used as the first integration time.

4. The method according to claim 1, characterized in that: The determining, according to the brightness of the background area in each of the integral imaging results, a second integration time corresponding to the background area comprises: According to the pixel points in the background area and whose brightness reaches saturation in each of the integral imaging results, respectively determining the saturation integration time when the brightness of each of the pixel points in the background area reaches saturation; According to the overexposure suppression strategy and the saturation integration time, the second integration time corresponding to each of the pixel points is determined respectively.

5. The method according to claim 1, characterized in that The step of reconstructing a plurality of target particle images corresponding to the particle region according to the integrated imaging results of each of the target region and the background region comprises: Determine, according to the pulse sampling time and the preset sampling duration, each imaging moment corresponding to the particle region within the preset sampling duration; Respectively determining a first integral imaging result corresponding to the integral imaging result of the background area at each of the imaging moments, and respectively determining a second integral imaging result corresponding to the integral imaging result of the target area at each of the imaging moments; Based on the first integral imaging result and the second integral imaging result, target particle images corresponding to each imaging moment are generated respectively.

6. The method according to claim 1, characterized in that The above method further includes: Based on the pulse emission frequency corresponding to each pixel point in the original pulse image data, the pixel difference between each two adjacent target particle images, and at least one of the first target filter and the second target filter, denoising each target particle image to obtain a denoised target particle image; The first target filter is constructed based on the target shape characteristics of the particles, and the second target filter is constructed based on the target motion characteristics of the particles.

7. The method according to claim 6, characterized in that The method further comprises: performing denoising processing on each of the target particle images based on the pixel difference between each two adjacent initial particle images, comprising: A difference processing is performed on any two adjacent frames of target particle images. When the pixel difference corresponding to any pixel point in the two frames of target particle images is greater than a first preset threshold and less than a second preset threshold, the pixel point is determined as a noise pixel point, and denoising is performed on each of the noise pixel points in the two frames of target particle images.

8. The method according to claim 6, characterized in that The target shape feature of the particle includes an area feature and an intensity feature, wherein the area feature is that the number of pixel points corresponding to a single particle in the particle image is greater than 1, and the intensity feature is that among all the pixel points corresponding to a single particle in the particle image, the intensity of the central pixel point is greater than the intensity of the edge pixel points; The target motion feature of the particle is that in the original pulse image data, the change values ​​of the pulse emission frequencies of a plurality of adjacent pixel points have a correlation relationship.

9. A particle image reconstruction device, characterized in that: The device comprises: An integration module is used to collect raw pulse image data for a particle region within a preset sampling time, perform integral imaging processing on the raw pulse image data using different integral times for multiple times, and determine a target region including particles and a background region excluding particles in the raw pulse image data according to each integral imaging result obtained; A determination module, used to determine the signal-to-noise ratio of each target area corresponding to different integration times, determine the first integration time corresponding to each target area according to each signal-to-noise ratio, and determine the second integration time corresponding to the background area according to the brightness of the background area in each integral imaging result; A reconstruction module is used to perform integral imaging processing on each of the target areas according to the first integration time, and to perform integral imaging processing on the background area according to the second integration time, and to reconstruct a multi-frame target particle image corresponding to the particle area based on the integral imaging results of each of the target areas and the background area.

10. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 8 are implemented.

11. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 8 are implemented.

Images