Image compression process enhancement method and device, equipment and readable storage medium

Abstract

The invention discloses an image compression process enhancement method, device and equipment and a readable storage medium, and the method comprises the steps: obtaining a to-be-processed basic layer based on the filtering processing of an original ultrasonic image with a high dynamic range; performing compression mapping processing on the to-be-processed base layer according to different gray mapping relationships corresponding to different adjustment parameters to obtain a plurality of detail enhancement base layers with different low dynamic ranges; calculating the image information entropy of each detail enhancement base layer, and determining a reference base layer from the detail enhancement base layers based on the calculated image information entropy; based on the feature map of the reference basic layer, carrying out optimization processing on pixels of the reference basic layer to obtain a target basic layer; obtaining a target ultrasonic image based on the target basic layer; the target ultrasonic image is an image with a low dynamic range. The method has the technical effects that dynamic range compression is carried out on the ultrasonic image, more detail information can be reserved, and the requirements of human eye observation and some applications are met.

Description

Technical Field

[0001] This application relates to the field of image processing technology, and in particular to an enhancement method, apparatus, device, and readable storage medium for an image compression process. Background Technology

[0002] In order to record the rich and subtle grayscale changes in ultrasound images as much as possible, modern ultrasound imaging systems typically have high dynamic range imaging capabilities, that is, they can output ultrasound image data with a dynamic range of 14 bits or 16 bits, which far exceeds the dynamic range of conventional display devices (usually 8 bits).

[0003] After performing non-uniformity correction on the ultrasound imaging system, the acquired raw high dynamic range data needs to be compressed into an 8-bit image. Commonly used high dynamic range ultrasound image visualization techniques can only perform simple compression mapping of high dynamic range gray levels, but cannot effectively retain the rich detail information in the original image. This greatly reduces the quality and display effect of the output 8-bit image, making it difficult to meet the needs of human visual observation and certain applications.

[0004] In summary, how to effectively solve the problem of dynamic range compression in ultrasound images is a technical problem that urgently needs to be solved by those skilled in the art. Summary of the Invention

[0005] The purpose of this application is to provide an enhancement method, apparatus, device, and readable storage medium for the image compression process, which can retain more detailed information when compressing the dynamic range of ultrasound images.

[0006] To solve the above-mentioned technical problems, this application provides the following technical solution:

[0007] An enhancement method for an image compression process includes:

[0008] Based on the filtering of the original ultrasound image with high dynamic range, the basic layer to be processed is obtained.

[0009] According to the different grayscale mapping relationships corresponding to different adjustment parameters, the base layer to be processed is compressed and mapped to obtain multiple different low dynamic range detail enhancement base layers.

[0010] Calculate the image information entropy of each detail enhancement base layer, and determine a reference base layer from the detail enhancement base layers based on the calculated image information entropy;

[0011] Based on the feature map of the reference base layer, the pixels of the reference base layer are optimized to obtain the target base layer.

[0012] Based on the target base layer, a target ultrasound image is obtained; the target ultrasound image is a low dynamic range image.

[0013] For example, based on the target base layer, a target ultrasound image is obtained, including:

[0014] Obtain the detail layer of the original ultrasound image;

[0015] Pixels in the detail layer whose amplitude exceeds the upper limit of the preset amplitude range are compressed and restricted, and pixels in the detail layer whose amplitude is lower than the lower limit of the preset amplitude range are stretched and enlarged to obtain an enhanced detail layer;

[0016] The target base layer and the enhanced detail layer are fused to obtain the target ultrasound image.

[0017] For example, fusing the target base layer with the enhanced detail layer to obtain the target ultrasound image includes:

[0018] Noise suppression is applied to the enhanced detail layer;

[0019] The target base layer is fused with the noise-suppressed enhanced detail layer to obtain the target ultrasound image.

[0020] For example, noise suppression of the enhanced detail layer includes:

[0021] Take all pixels in the enhanced detail layer as the first pixel and perform the following operations:

[0022] A first window is defined in the enhanced detail layer, centered on the first pixel in the enhanced detail layer;

[0023] In the first window, identify a second pixel that is different from the first pixel;

[0024] A second window is defined in the enhanced detail layer, centered on the second pixel.

[0025] Calculate the correlation between the first window and the second window to obtain the correlation value between the first pixel and the second pixel;

[0026] Calculate the noise suppression factor based on the correlation value;

[0027] Based on the noise suppression factor, the pixel value of the first pixel is updated;

[0028] After all pixels have undergone the above operations, the enhanced detail layer with noise suppression is obtained.

[0029] For example, fusing the target base layer with the enhanced detail layer to obtain the target ultrasound image includes:

[0030] Feature information is extracted from the target base layer, and adaptive weights are determined using the feature information;

[0031] The target base layer and the enhanced detail layer are fused using the adaptive weights to obtain the target ultrasound image.

[0032] For example, extracting feature information from the target base layer and using the feature information to determine adaptive weights includes:

[0033] Based on the feature extraction function, the pixel value corresponding to any pixel in the target base layer is compared with the reference pixel value, and the pixel feature value is extracted from the target base layer based on the comparison result to obtain the feature information; wherein, the reference pixel value is a pixel value calculated based on the maximum pixel value in the target base layer;

[0034] The adaptive weights are calculated based on the maximum and minimum values ​​corresponding to the feature information.

[0035] For example, based on filtering of the original ultrasound image with high dynamic range, a basic layer to be processed is obtained, including:

[0036] The original ultrasound image is subjected to multi-scale filtering to obtain initial basic layers corresponding to different scales;

[0037] Subtract the initial base layer corresponding to different scales from the original ultrasound image to obtain the initial detail layer corresponding to different scales.

[0038] The initial detail layers at different scales are weighted and fused to obtain the reference detail layer;

[0039] The reference detail layer is subtracted from the original ultrasound image to obtain the base layer to be processed.

[0040] For example, determining a reference base layer from the detail enhancement base layer based on the calculated image information entropy includes:

[0041] The maximum image information entropy is determined from the calculated image information entropy;

[0042] The detail enhancement base layer corresponding to the maximum image information entropy is determined as the reference base layer.

[0043] For example, the feature map is a grayscale feature histogram;

[0044] Based on the feature map of the reference base layer, the pixels of the reference base layer are optimized to obtain the target base layer, including:

[0045] The initial gray-level feature histogram of the reference base layer is subjected to equalization processing to obtain an equalized gray-level feature histogram.

[0046] The initial gray-level feature histogram is corrected and integrated based on the initial gray-level feature histogram and the balanced gray-level feature histogram to obtain the corrected gray-level feature histogram.

[0047] Based on the corrected grayscale feature histogram, the reference base layer is modified to optimize the pixels of the reference base layer, thereby obtaining the target base layer.

[0048] For example, according to different grayscale mapping relationships corresponding to different adjustment parameters, the base layer to be processed is subjected to compression mapping processing to obtain multiple different low dynamic range detail enhancement base layers, including:

[0049] According to different adjustment parameters, the gray-level feature histogram of the base layer to be processed is modified to obtain several modified gray-level feature histograms.

[0050] Based on the gray-level mapping relationship corresponding to different modified gray-level feature histograms, the base layer to be processed is subjected to compression mapping processing to obtain multiple different low dynamic range detail enhancement base layers.

[0051] An enhancement device for an image compression process, comprising:

[0052] The filtering module is used to obtain the basic layer to be processed based on the filtering processing of the original ultrasound image with high dynamic range;

[0053] The compression module is used to perform compression mapping processing on the base layer to be processed according to different grayscale mapping relationships corresponding to different adjustment parameters, so as to obtain multiple different low dynamic range detail enhancement base layers.

[0054] A filtering module is used to calculate the image information entropy of each detail enhancement base layer and determine a reference base layer from the detail enhancement base layers based on the calculated image information entropy;

[0055] An optimization module is used to optimize the pixels of the reference base layer based on the feature map of the reference base layer to obtain the target base layer.

[0056] The output module is used to obtain a target ultrasound image based on the target base layer; the target ultrasound image is a low dynamic range image.

[0057] An electronic device, comprising:

[0058] Memory, used to store computer programs;

[0059] A processor is configured to implement the steps of the above-described image compression process enhancement method when executing the computer program.

[0060] A readable storage medium storing a computer program that, when executed by a processor, implements the steps of the above-described image compression enhancement method.

[0061] The method provided in this application includes: obtaining a base layer to be processed based on filtering of a high dynamic range original ultrasound image; performing compression mapping processing on the base layers to be processed according to different gray-level mapping relationships corresponding to different adjustment parameters to obtain multiple different low dynamic range detail enhancement base layers; calculating the image information entropy of each detail enhancement base layer, and determining a reference base layer from the detail enhancement base layers based on the calculated image information entropy; optimizing the pixels of the reference base layer based on the feature map of the reference base layer to obtain a target base layer; and obtaining a target ultrasound image based on the target base layer; the target ultrasound image is a low dynamic range image.

[0062] In the process of dynamic range compression of the original high dynamic range ultrasound image, filtering is first performed to obtain the base layer to be processed. This base layer has a similar high dynamic range to the original ultrasound image, therefore dynamic range compression adjustment is required to maintain good image quality in the resulting low dynamic range image. The base layer is compressed using a gray-level mapping relationship. However, since the background component accounts for a large proportion of the base layer, the gray-level mapping relationship often contains significant background noise, easily leading to gray-level saturation in the resulting image. Therefore, this application performs compression mapping on the base layer to be processed according to different gray-level mapping relationships corresponding to different adjustment parameters, thereby obtaining multiple low dynamic range detail enhancement base layers. Then, a reference base layer is selected from these detail enhancement base layers based on image information entropy. Based on the feature map of the reference base layer, the pixels of the reference base layer are optimized to obtain the target base layer. Based on this target base layer, a low dynamic range target ultrasound image with good image quality can be obtained.

[0063] Technical Effects: During the compression of raw ultrasound images with high dynamic range, after detail enhancement processing of the base layer, image information entropy is used to determine a reference base layer from multiple detail-enhanced base layers. This reference base layer can retain more detail. Furthermore, the pixels of the reference base layer are optimized based on feature maps, allowing the final target ultrasound image based on the target base layer to retain more detail information, meeting the needs of human visual observation and certain applications.

[0064] Accordingly, embodiments of this application also provide an image compression apparatus, device, and readable storage medium corresponding to the above-described image compression process enhancement method, which have the above-described technical effects, and will not be repeated here. Attached Figure Description

[0065] To more clearly illustrate the technical solutions in the embodiments or related technologies of this application, the accompanying drawings used in the description of the embodiments or related technologies will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0066] Figure 1 This is a flowchart illustrating an implementation method for enhancing an image compression process according to an embodiment of this application.

[0067] Figure 2 This is a schematic diagram of a multi-scale filtering method in an embodiment of this application;

[0068] Figure 3 This is a schematic diagram of the structure of an enhancement device for an image compression process according to an embodiment of this application;

[0069] Figure 4 This is a schematic diagram of the structure of an electronic device according to an embodiment of this application;

[0070] Figure 5 This is a schematic diagram of the specific structure of an electronic device in an embodiment of this application. Detailed Implementation

[0071] To enable those skilled in the art to better understand the present application, the present application will be further described in detail below with reference to the accompanying drawings and specific embodiments. Obviously, the described embodiments are merely some embodiments of the present application, and not all embodiments. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0072] Please refer to Figure 1 , Figure 1 This is a flowchart of an image compression enhancement method according to an embodiment of this application. The method includes the following steps:

[0073] S101. Based on the filtering processing of the original ultrasound image with high dynamic range, the basic layer to be processed is obtained.

[0074] The original ultrasound image is 14-bit or 16-bit high dynamic range ultrasound image data output by the ultrasound imaging system.

[0075] In this embodiment, the original ultrasound image can be filtered to obtain its base layer. To facilitate differentiation between base layers under different subsequent processing states, the base layer obtained through filtering is referred to as the base layer to be processed in this embodiment.

[0076] In this embodiment, when filtering the original ultrasound image, the base layer can be directly filtered out based on the filtering algorithm, or the detail layer can be filtered out first, and the difference between the original ultrasound image and the detail layer can be calculated to obtain the base layer based on the difference.

[0077] In one specific embodiment of this application, a basic layer to be processed is obtained based on filtering of the original ultrasound image with high dynamic range, including:

[0078] Step 1: Perform multi-scale filtering on the original ultrasound image to obtain the initial basic layers corresponding to different scales;

[0079] Step 2: Subtract the initial base layer corresponding to different scales from the original ultrasound image to obtain the initial detail layer corresponding to different scales.

[0080] Step 3: Weighted fusion of initial detail layers at different scales to obtain a reference detail layer;

[0081] Step 4: Subtract the reference detail layer from the original ultrasound image to obtain the base layer to be processed.

[0082] For ease of description, the above four steps will be explained in combination below.

[0083] The input high dynamic range raw ultrasound image is decomposed into a base layer and a detail layer using a filtering algorithm. The base layer is a smoothed version of the input image after filtering, and the detail layer is obtained by subtracting the base layer from the input image.

[0084] The inventors discovered that performing a single-scale decomposition on an image is insufficient to comprehensively capture its high-frequency details, as much important information is reflected at different scales. Therefore, in this embodiment, multi-scale filtering is employed, and the base layer and detail layer obtained from filtering at different scales are fused to obtain base and detail layers with more detailed content.

[0085] In other words, multi-scale filtering fusion processing refers to first performing multi-scale filtering, then fusing the filtering results at different scales to finally obtain a basic layer and a detail layer that cover the details that can be preserved by filtering at different scales.

[0086] In the image decomposition process of multi-scale filtering fusion, other low-pass filters can also be used, such as Gaussian filters, guided filters, etc. The filter parameters can be set according to the image effect.

[0087] Please refer to Figure 2 For the original dynamic ultrasound image I, it is first subjected to multi-scale filtering as described below:

[0088]

[0089] Where l represents the l-th decomposition scale, which is a preset parameter with a value range of l = 1, 2, ..., L; F l (·) represents the l-th filter, whose scale varies from fine to coarse; I represents a high dynamic range ultrasound image, B l and D l These are the initial base layer and initial detail layer for the l-th image, respectively; initialize B0 = I.

[0090] The mathematical formula for the filtering process is: Among them, I p The coordinates of image I are represented by p = (p x ,p y The pixel value at position I q F(I) represents the neighboring pixel values ​​of p. p ) represents the result of convolving the image at point p with filter F (i.e., summing the dot product of the filter weights and the pixel values ​​within the filter window); ||pq|| represents the Euclidean distance between p and q, |I p -I q | represents the absolute value of the difference between the pixel values ​​at points p and q; and Represents the weights of a two-dimensional filter. σ s Define the filter size; normalization coefficients Ensure the sum of the filter weights is 1.0. σ s and σ r These are preset parameters.

[0091] The distance and absolute value between p and q are used as factors in calculating the filter weights, taking into account both the positional relationship between the center point and other points in the filter window and the numerical relationship.

[0092] The processed initial detail layers are weighted and fused (to ensure consistent pixel value ranges after weighted fusion, weight values ​​range from 0 to 1, and the sum of weight values ​​equals 1) to generate a fused reference detail layer D. Then, the reference detail layer D is subtracted from the original image I to obtain the base layer B to be processed. Thus, B and D serve as the layering result for the high dynamic range ultrasound image I. This multi-scale detail fusion layering process is expressed by the formula:

[0093] Where, α l is the fusion weight for the l-th scale, which is a preset parameter.

[0094] After image decomposition, dynamic range adjustment and optimization are needed for both the base layer and detail layer to improve image contrast and effectively highlight the information contained therein. Based on the grayscale distribution characteristics of the base layer and detail layer, different methods are used for detail enhancement.

[0095] S102. According to the different grayscale mapping relationships corresponding to different adjustment parameters, the base layers to be processed are compressed and mapped respectively to obtain multiple different low dynamic range detail enhancement base layers.

[0096] In this embodiment, to obtain more detailed enhancement base layers, multiple different adjustment parameters can be set. After obtaining the base layers to be processed, compression mapping processing is performed on each base layer according to the different grayscale mapping relationships corresponding to the different adjustment parameters, thereby obtaining multiple different low dynamic range detail enhancement base layers. The grayscale mapping relationship can be a mapping function that implements compression mapping, capable of mapping high dynamic range ultrasound images to low dynamic range ultrasound images. Furthermore, during the compression enhancement process of the base layers, the expression of the mapping function can be adjusted according to the actual image processing requirements.

[0097] For example, 'a' adjustment parameters can be preset. Correspondingly, different sets of adjustment parameters correspond to different grayscale mapping relationships, meaning there are also 'a' grayscale mapping relationships. These 'a' different grayscale mapping relationships are used to perform compression mapping processing on the base layer to be processed, resulting in 'a' different low dynamic range detail enhancement base layers. Subsequently, the best detail enhancement base layer can be selected from these 'a' detail enhancement base layers for further processing.

[0098] In one specific embodiment of this application, compression mapping processing is performed on the base layer to be processed according to different grayscale mapping relationships corresponding to different adjustment parameters, resulting in multiple different low dynamic range detail enhancement base layers, including:

[0099] Step 1: According to different adjustment parameters, the gray-level feature histograms of the base layer to be processed are corrected to obtain several corrected gray-level feature histograms.

[0100] Step 2: According to the gray-level mapping relationship corresponding to different modified gray-level feature histograms, perform compression mapping processing on the base layer to be processed to obtain multiple different low dynamic range detail enhancement base layers.

[0101] For ease of description, the two steps above will be explained together below.

[0102] The gray-level feature histogram of the base layer to be processed is modified to obtain several modified gray-level feature histograms.

[0103] The base layer to be processed has a similar high dynamic range to the original ultrasound image, therefore dynamic range compression adjustment is required, and the resulting low-dynamic image should maintain good image quality, such as high image contrast. A gray-level mapping method based on histogram equalization is used for the base layer to be processed, with the histogram cumulative distribution function as the mapping function, which effectively improves the contrast of the output result. Since the background component accounts for a large proportion of the base layer, the image histogram often contains significant background gray-level spikes, which causes the cumulative distribution function to have a large slope change, easily leading to gray-level saturation effects in the resulting image.

[0104] Therefore, in this embodiment, a logarithmic-power transformation method can be used to correct the gray-level feature histogram so that the corrected gray-level feature histogram can be used for equalized gray-level mapping.

[0105] The specific process of performing a logarithmic-power transformation on the gray-level feature histogram of the base layer to be processed is as follows:

[0106] Where H and H′ are the original gray-level feature histogram and the modified gray-level feature histogram, respectively; j is the high dynamic range gray level, assuming the dynamic range of the original ultrasound image is 14 bits, then j ranges from 0 to 16384; β is a preset parameter, β ≥ 1. This nonlinear transformation effectively preserves the basic distribution of the original gray-level feature histogram while weakening potential background gray-level spikes, thus making the histogram distribution more uniform. Using the modified gray-level feature histogram H′, its cumulative distribution function can be calculated as follows:

[0107] In the formula, PDF and CDF represent the probability density function and cumulative distribution function of the gray-level feature histogram, respectively. Therefore, the gray-level mapping relationship between high and low dynamic ranges can be obtained based on the cumulative distribution function as follows: In the formula, r is the mapped low dynamic gray level. This indicates rounding down. Using the above mapping relationship, the high dynamic range base layer B to be processed can be compressed and mapped to an 8-bit low dynamic range base layer B′.

[0108] It should be noted that in the correction of the basic layer to be processed by logarithmic-power transformation, the expressions of the logarithmic function and the power function can be adjusted according to the actual image processing needs, but the processing purpose is the same, which is to correct the gray-level feature histogram.

[0109] S103. Calculate the image information entropy of each detail enhancement base layer, and determine the reference base layer from the detail enhancement base layers based on the calculated image information entropy.

[0110] Image information entropy can effectively reflect the richness of information contained in an image. Therefore, in this embodiment, image information entropy is used to select a reference base layer from several detail enhancement base layers. The selection criterion can be to use the detail enhancement base layer with the highest information richness as the reference base layer.

[0111] In one embodiment of this application, determining a reference base layer from a detail enhancement base layer based on the calculated image information entropy includes:

[0112] The maximum image information entropy is determined from the calculated image information entropy;

[0113] The detail enhancement base layer corresponding to the maximum image information entropy is determined as the reference base layer.

[0114] For ease of description, the two steps above will be explained together below.

[0115] To adaptively set a better β value (i.e., adjust the parameter), a selection strategy based on maximizing entropy is adopted. Image information entropy can effectively reflect the richness of information contained in an image. Using image information entropy to guide the setting of the β value, the detail enhancement base layer with the maximum entropy is used as the output of the base layer to be processed, that is, the detail enhancement base layer with the maximum image information entropy is used as the reference base layer.

[0116] Specifically, since accurately calculating the β value corresponding to the maximum entropy is quite complex, a method is adopted where several discrete β values ​​are pre-defined. The β value corresponding to the maximum entropy is found by calculating the information entropy of each of these values, and then the result is used as the output. This process can be expressed by the following formula:

[0117] In the formula, IE(·) represents the image information entropy.

[0118] S104. Based on the feature map of the reference base layer, optimize the pixels of the reference base layer to obtain the target base layer.

[0119] In this application, the target base layer can be obtained by optimizing the pixels within a reference base layer based on its feature map. This optimization can include visual effect enhancement, such as pixel feature enhancement or correction.

[0120] In one specific embodiment of this application, the feature map is a grayscale feature histogram;

[0121] Based on the feature map of the reference base layer, the pixels of the reference base layer are optimized to obtain the target base layer, including:

[0122] Step 1: Equalize the initial gray-level feature histogram of the reference base layer to obtain an equalized gray-level feature histogram.

[0123] Step 2: Based on the initial gray-level feature histogram and the balanced gray-level feature histogram, the initial gray-level feature histogram is corrected and integrated to obtain the corrected gray-level feature histogram.

[0124] Step 3: Based on the corrected grayscale feature histogram, the reference base layer is corrected to optimize the pixels of the reference base layer and obtain the target base layer.

[0125] For ease of description, the three steps mentioned above will be explained together below.

[0126] The visual effect of the 8-bit low-dynamic base layer B′ is adaptively improved by using a modified grayscale feature histogram optimized based on the evaluation metric. The specific method is as follows:

[0127] The initial gray-level feature histogram of B′ is H0, the equalized gray-level feature histogram after standard equalization is H1=HE(H0), and the corrected gray-level feature histogram is... by Histogram correction is applied to B′ to obtain the target base layer B″. Among them, δ is a preset parameter; when Δ = 0 That is, perform standard histogram equalization on B′; when Δ = 1 That is, no modifications are made to the histogram of B′.

[0128] To adaptively adjust the visual effect of B′, evaluation metrics are used to assess the difference between the histogram-processed results B″ and B′. These metrics include, but are not limited to, structural similarity (SSIM) and peak signal-to-noise ratio (PSNR). Here, SSIM is used as an example, and the Δ corresponding to the maximum SSIM value is taken as the optimal parameter setting for the algorithm. The formula is as follows:

[0129]

[0130] in, This represents the optimal Δ value. Several discrete Δ values ​​are pre-defined, and the desired SSIM (Sum of Simulation) of the corresponding results and the original image is calculated. The corresponding result is then used as the algorithm output.

[0131] S105. Based on the target base layer, obtain the target ultrasound image; wherein, the target ultrasound image is a low dynamic range image.

[0132] After obtaining the target base layer, the target base layer is merged with the compressed detail layer to obtain the target ultrasound image with low dynamic range. In some embodiments, the image corresponding to the target base layer can also be directly used as the target ultrasound image.

[0133] The method provided in this application includes: obtaining a base layer to be processed based on filtering of a high dynamic range original ultrasound image; performing compression mapping processing on the base layers to be processed according to different gray-level mapping relationships corresponding to different adjustment parameters to obtain multiple different low dynamic range detail enhancement base layers; calculating the image information entropy of each detail enhancement base layer, and determining a reference base layer from the detail enhancement base layers based on the calculated image information entropy; optimizing the pixels of the reference base layer based on the feature map of the reference base layer to obtain a target base layer; and obtaining a target ultrasound image based on the target base layer; the target ultrasound image is a low dynamic range image.

[0134] In the process of dynamic range compression of the original high dynamic range ultrasound image, filtering is first performed to obtain the base layer to be processed. This base layer has a similar high dynamic range to the original ultrasound image, therefore dynamic range compression adjustment is necessary to maintain good image contrast in the generated low dynamic range result. Applying gray-level mapping based on histogram equalization to the base layer, using the histogram cumulative distribution function as the mapping function, can effectively improve the contrast of the output result. However, since the background component accounts for a large proportion of the base layer, the image histogram often contains significant background gray-level peaks, resulting in a large slope change in the cumulative distribution function, which can easily lead to gray-level saturation in the resulting image. Therefore, this application performs compression mapping processing on the base layer to be processed according to the gray-level mapping relationship corresponding to different adjustment parameters, thereby obtaining multiple low dynamic range detail enhancement base layers. Then, a reference base layer is selected from these detail enhancement base layers based on image information entropy. Based on the feature map of the reference base layer, the pixels of the reference base layer are optimized to obtain the target base layer. Based on this target base layer, the target ultrasound image with low dynamic range can be obtained.

[0135] Technical Effects: During the compression of raw ultrasound images with high dynamic range, after detail enhancement processing of the base layer, image information entropy is used to determine a reference base layer from multiple detail-enhanced base layers. This reference base layer can retain more detail. Furthermore, the pixels of the reference base layer are optimized based on feature maps, allowing the final target ultrasound image based on the target base layer to retain more detail information, meeting the needs of human visual observation and certain applications.

[0136] It should be noted that, based on the above embodiments, the embodiments of this application also provide corresponding improvement schemes. In the preferred / improved embodiments, the same or corresponding steps as in the above embodiments can be referred to each other, and the corresponding beneficial effects can also be referred to each other; however, these will not be elaborated upon in the preferred / improved embodiments herein.

[0137] In one specific embodiment of this application, obtaining a target ultrasound image based on the target base layer includes:

[0138] Step 1: Obtain the detail layer of the original ultrasound image;

[0139] Step 2: Compress and limit the pixels in the detail layer whose amplitude exceeds the upper limit of the preset amplitude range, and stretch and enlarge the pixels in the detail layer whose amplitude is lower than the lower limit of the preset amplitude range to obtain the enhanced detail layer;

[0140] Step 3: Fuse the target base layer and the enhanced detail layer to obtain the target ultrasound image.

[0141] For ease of description, the above steps will be combined below.

[0142] Filtering can be used to obtain the detailed layers of the original ultrasound image.

[0143] The upper and lower limits of the preset amplitude range can be specific amplitude values ​​set in advance; alternatively, they can be functions with a range of values, where the upper and lower limits of the function's range are used as the upper and lower limits of the preset amplitude range, respectively. Furthermore, the absolute values ​​of the upper and lower limits of the preset amplitude range can differ significantly or be approximately equal, depending on the specific circumstances.

[0144] It should be noted that for the detail layer, its pixel values ​​fluctuate around zero, with roughly equal maximum positive and negative deviations. Pixels with larger amplitudes are considered to have stronger edge structures and potential noise, while pixels with smaller amplitudes mainly represent subtle texture details. Therefore, optimizing the detail layer should compress and restrict pixels with larger amplitudes while stretching and amplifying pixels with smaller amplitudes, and the transformation function should be smooth and symmetrical about zero. Specifically, a mapping function with a range of [-1, 1] is designed to compress and enhance the detail layer. The specific form of this function is as follows:

[0145] In the formula, D′ is the compressed and enhanced detail layer, and c and ε are preset parameters for adjusting the shape of the function curve.

[0146] In one specific embodiment of this application, the target base layer and the enhanced detail layer are fused to obtain a target ultrasound image, including:

[0147] Noise suppression is applied to the enhancement detail layer;

[0148] The target base layer is fused with the noise-suppressed enhancement detail layer to obtain the target ultrasound image.

[0149] This includes noise suppression of the enhanced detail layer, including:

[0150] Treat all pixels in the enhancement detail layer as the first pixel and perform the following operations:

[0151] A first window is defined in the enhancement detail layer, centered on the first pixel.

[0152] In the first window, identify the second pixel that is different from the first pixel.

[0153] A second window is defined in the detail enhancement layer, centered on the second pixel.

[0154] Calculate the correlation between the first window and the second window to obtain the correlation value between the first pixel and the second pixel;

[0155] Calculate the noise suppression factor based on the correlation value;

[0156] The pixel value of the first pixel is updated based on the noise suppression factor;

[0157] After all pixels have undergone the above operations, the enhanced detail layer with noise suppression is obtained.

[0158] Specifically, noise suppression is performed on the enhanced detail layer image D′, that is, the following processing is performed on all pixels in D′ to obtain D″:

[0159] For a point A (i.e., the first pixel) in D′, its n×n neighborhood is A n Centered on point A, take a window W of size w×h (w≤r,h≤c) in D′ (i.e., the first window); for a point B in W (i.e., the second pixel), its n×n neighborhood is B. n (i.e., the second window) calculate matrix A n With B n The correlation can be calculated using methods such as mean squared error (MSE), peak signal-to-noise ratio (PSNR), and structural similarity (SSIM), yielding a scalar v(A,B) (i.e., the correlation value between the first and second pixels), which is then used to calculate factors. (i.e., noise suppression factor), h is a preset parameter; perform the same operation as above on all points in W to obtain the processed I. A =∑f(A,B)·I B I A and I BThese are the pixel values ​​for points A and B, respectively.

[0160] For example, the above processing can be performed on the pixels in D′ in a certain order to obtain D″. For instance, each pixel can be taken as the first pixel in the order of pixels from top to bottom and from left to right, thereby completing the correlation calculation and pixel update.

[0161] In one specific embodiment of this application, the target base layer and the enhanced detail layer are fused to obtain a target ultrasound image, including:

[0162] Step 1: Extract feature information from the target base layer and use the feature information to determine adaptive weights;

[0163] Step 2: Using adaptive weights, the target base layer and the enhanced detail layer are fused to obtain the target ultrasound image.

[0164] In this embodiment, after the detail enhancement processing of the base layer and the detail layer is completed, the base layer and the detail layer can be fused to obtain a dynamic ultrasound image of the target after dynamic range compression.

[0165] The target dynamic ultrasound image is a low dynamic range ultrasound image that can be displayed on a display device.

[0166] During the fusion process, weighted fusion can be performed, and the adaptive weights can be determined based on the feature information of the target base layer. Based on the adaptive weights, the weights of the target base layer and the enhancement detail layer can be determined separately, and the sum of the weights of the target base layer and the enhancement detail layer is 1. Taking an adaptive weight of w1 as an example, the weight of the target base layer can be determined as w1, and the weight of the enhancement detail layer can be determined as 1-w1.

[0167] For example, corresponding pixels in the target base layer and the enhanced detail layer can be fused separately, that is, pixel-by-pixel fusion can be performed.

[0168] In one specific embodiment of this application, feature information is extracted from the target base layer, and adaptive weights are determined using the feature information, including:

[0169] Step 1: Based on the feature extraction function, compare the pixel value corresponding to any pixel in the target base layer with the reference pixel value, and extract the pixel feature value from the target base layer based on the comparison result to obtain feature information; wherein, the reference pixel value is the pixel value calculated based on the maximum pixel value in the target base layer;

[0170] Step 2: Calculate the adaptive weights based on the maximum and minimum values ​​corresponding to the feature information.

[0171] Among them, extracting pixel feature values ​​from the target base layer based on the comparison results can be done by extracting the maximum value between any pixel and the reference pixel value, with each pixel corresponding to a result, and combining these results as feature information.

[0172] For example, for a specific pixel to be fused, an adaptive weight can be calculated based on the feature information corresponding to that pixel, as well as the maximum and minimum values ​​determined among all feature information. Then, fusion is performed based on this adaptive weight, and after fusing all pixels in the target base layer and the enhanced detail layer, the target ultrasound image is obtained.

[0173] For example, a weighted adaptive image fusion method is used to merge the target base layer B′ and the detail layer D′ to construct a low dynamic range target ultrasound image I′ with enhanced details. The specific method is as follows:

[0174] First, the function F(p) = max[μB' is used. max Extracting effective information (pixel feature values) from B′(p), where B′(p) represents a point in B′, and B′... max This represents the maximum value of B′, where μ is a preset parameter. Furthermore, Where w(p) is the adaptive weight at point p, F max and F min These are the maximum and minimum values ​​of the feature map F;

[0175] Finally, w(p) is used to fuse the base layer and the detail layer, a process expressed by the formula:

[0176] In the formula, This indicates pixel stretching, which involves linearly stretching the image to the range of 0 to 255. This step can effectively enhance image contrast and make the image clearer.

[0177] Corresponding to the above method embodiments, this application also provides an image compression process enhancement device. The image compression process enhancement device described below and the image compression process enhancement method described above can be referred to in correspondence.

[0178] See Figure 3 As shown, the device includes the following modules:

[0179] The filtering module 101 is used to obtain the basic layer to be processed based on the filtering processing of the original ultrasound image with high dynamic range.

[0180] Compression module 102 is used to perform compression mapping processing on the base layer to be processed according to different grayscale mapping relationships corresponding to different adjustment parameters, so as to obtain multiple different low dynamic range detail enhancement base layers.

[0181] The filtering module 103 is used to calculate the image information entropy of each detail enhancement base layer and determine the reference base layer from the detail enhancement base layers based on the calculated image information entropy.

[0182] Optimization module 104 is used to optimize the pixels of the reference base layer based on the feature map of the reference base layer to obtain the target base layer.

[0183] Output module 105 is used to obtain a target ultrasound image based on the target base layer; the target ultrasound image is a low dynamic range image.

[0184] The apparatus provided in this application includes: obtaining a base layer to be processed based on filtering of a raw ultrasound image with high dynamic range; performing compression mapping processing on the base layers to be processed according to different grayscale mapping relationships corresponding to different adjustment parameters to obtain multiple different low dynamic range detail enhancement base layers; calculating the image information entropy of each detail enhancement base layer, and determining a reference base layer from the detail enhancement base layers based on the calculated image information entropy; optimizing the pixels of the reference base layer based on the feature map of the reference base layer to obtain a target base layer; and obtaining a target ultrasound image based on the target base layer; the target ultrasound image is a low dynamic range image.

[0185] In the process of dynamic range compression of the original high dynamic range ultrasound image, filtering is first performed to obtain the base layer to be processed. This base layer has a similar high dynamic range to the original ultrasound image, therefore dynamic range compression adjustment is necessary to maintain good image contrast in the generated low dynamic range result. Applying gray-level mapping based on histogram equalization to the base layer, using the histogram cumulative distribution function as the mapping function, can effectively improve the contrast of the output result. However, since the background component accounts for a large proportion of the base layer, the image histogram often contains significant background gray-level peaks, resulting in a large slope change in the cumulative distribution function, which can easily lead to gray-level saturation in the resulting image. Therefore, this application performs compression mapping processing on the base layer to be processed according to the gray-level mapping relationship corresponding to different adjustment parameters, thereby obtaining multiple low dynamic range detail enhancement base layers. Then, a reference base layer is selected from these detail enhancement base layers based on image information entropy. Based on the feature map of the reference base layer, the pixels of the reference base layer are optimized to obtain the target base layer. Based on this target base layer, the target ultrasound image with low dynamic range can be obtained.

[0186] Technical Effects: During the compression of raw ultrasound images with high dynamic range, after detail enhancement processing of the base layer, image information entropy is used to determine a reference base layer from multiple detail-enhanced base layers. This reference base layer can retain more detail. Furthermore, the pixels of the reference base layer are optimized based on feature maps, allowing the final target ultrasound image based on the target base layer to retain more detail information, meeting the needs of human visual observation and certain applications.

[0187] In one specific embodiment of this application, the output module specifically includes:

[0188] The detail layer acquisition unit is used to acquire the detail layer of the original ultrasound image;

[0189] The detail enhancement unit is used to compress and limit pixels in the detail layer whose amplitude exceeds the upper limit of the preset amplitude range, and to stretch and enlarge pixels in the detail layer whose amplitude is lower than the lower limit of the preset amplitude range, so as to obtain an enhanced detail layer.

[0190] The fusion unit is used to fuse the target base layer and the enhancement detail layer to obtain the target ultrasound image.

[0191] In one specific embodiment of this application, the fusion unit includes:

[0192] The noise suppression subunit is used to suppress noise in the enhanced detail layer;

[0193] The fusion subunit is used to fuse the target base layer with the noise-suppressed enhancement detail layer to obtain the target ultrasound image.

[0194] In one specific embodiment of this application, the noise suppression subunit is specifically used to treat all pixels in the enhanced detail layer as first pixels and perform the following operations:

[0195] A first window is defined in the enhancement detail layer, centered on the first pixel.

[0196] In the first window, identify the second pixel that is different from the first pixel.

[0197] A second window is defined in the detail enhancement layer, centered on the second pixel.

[0198] Calculate the correlation between the first window and the second window to obtain the correlation value between the first pixel and the second pixel;

[0199] Calculate the noise suppression factor based on the correlation value;

[0200] The pixel value of the first pixel is updated based on the noise suppression factor;

[0201] After all pixels have undergone the above operations, the enhanced detail layer with noise suppression is obtained.

[0202] In one specific embodiment of this application, the fusion subunit is specifically used to extract feature information from the target base layer and use the feature information to determine adaptive weights;

[0203] By using adaptive weights, the target base layer and the enhanced detail layer are fused to obtain the target ultrasound image.

[0204] In one specific embodiment of this application, the fusion subunit is specifically used to compare the pixel value corresponding to any pixel point in the target base layer with the reference pixel value based on the feature extraction function, and extract the pixel feature value from the target base layer based on the comparison result to obtain feature information; wherein, the reference pixel value is the pixel value calculated based on the maximum pixel value in the target base layer;

[0205] The adaptive weights are calculated based on the maximum and minimum values ​​corresponding to the feature information.

[0206] In one specific embodiment of this application, the filtering module specifically includes:

[0207] The multi-scale filtering unit is used to perform multi-scale filtering on the original ultrasound image to obtain the initial basic layer corresponding to different scales.

[0208] The detail layer acquisition unit is used to subtract the initial base layer corresponding to different scales from the original ultrasound image to obtain the initial detail layer corresponding to different scales.

[0209] The reference detail layer acquisition unit is used to weighted fuse initial detail layers of different scales to obtain the reference detail layer;

[0210] The basic layer acquisition unit is used to subtract the reference detail layer from the original ultrasound image to obtain the basic layer to be processed.

[0211] In one specific embodiment of this application, the filtering module is specifically used to determine the maximum image information entropy from the calculated image information entropy;

[0212] The detail enhancement base layer corresponding to the maximum image information entropy is determined as the reference base layer.

[0213] In one specific embodiment of this application, the feature map is a grayscale feature histogram;

[0214] The optimization module includes:

[0215] The equalization unit is used to equalize the initial gray-level feature histogram of the reference base layer to obtain an equalized gray-level feature histogram.

[0216] The correction unit is used to correct and integrate the initial gray-level feature histogram based on the initial gray-level feature histogram and the balanced gray-level feature histogram to obtain the corrected gray-level feature histogram.

[0217] The optimization unit is used to modify the reference base layer based on the corrected gray-scale feature histogram to optimize the pixels of the reference base layer and obtain the target base layer.

[0218] In one specific embodiment of this application, the compression module is specifically used to correct the gray-level feature histograms of the base layer to be processed according to different adjustment parameters, so as to obtain a number of corrected gray-level feature histograms.

[0219] Based on the gray-level mapping relationship corresponding to different modified gray-level feature histograms, the base layer to be processed is compressed and mapped to obtain multiple different low dynamic range detail enhancement base layers.

[0220] Corresponding to the above method embodiments, this application also provides an electronic device. The electronic device described below and the image compression process enhancement method described above can be referred to in correspondence.

[0221] See Figure 4 As shown, the electronic device includes:

[0222] Memory 332 is used to store computer programs;

[0223] The processor 322 is configured to implement the steps of the enhanced method for the image compression process of the above method embodiments when executing a computer program.

[0224] For details, please refer to Figure 5 , Figure 5 This is a schematic diagram of a specific structure of an electronic device provided in this embodiment. The electronic device can vary significantly due to differences in configuration or performance. It may include one or more central processing units (CPUs) (e.g., one or more processors) and a memory 332. The memory 332 stores one or more computer programs 342 or data 344. The memory 332 can be temporary or permanent storage. The program stored in the memory 332 may include one or more modules (not shown in the diagram), each module may include a series of instruction operations on the data processing device. Furthermore, the processor 322 may be configured to communicate with the memory 332 and execute the series of instruction operations stored in the memory 332 on the electronic device 301.

[0225] Electronic device 301 may also include one or more power supplies 326, one or more wired or wireless network interfaces 350, one or more input / output interfaces 358, and / or one or more operating systems 341.

[0226] The steps in the image compression enhancement method described above can be implemented by the structure of an electronic device.

[0227] Corresponding to the above method embodiments, this application also provides a readable storage medium. The readable storage medium described below and the image compression process enhancement method described above can be referred to in relation to each other.

[0228] A readable storage medium storing a computer program, which, when executed by a processor, implements the steps of the enhanced method for the image compression process described in the above method embodiments.

[0229] Specifically, the readable storage medium can be a USB flash drive, a portable hard drive, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk, or any other readable storage medium capable of storing program code.

[0230] According to another aspect of the present invention, a computer program product is also provided, including computer program instructions that, when executed, perform the enhanced method for the image compression process described above.

[0231] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on its differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably. For the apparatus disclosed in the embodiments, since it corresponds to the method disclosed in the embodiments, the description is relatively simple; relevant parts can be referred to in the method section.

[0232] Those skilled in the art will further recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of both. To clearly illustrate the interchangeability of hardware and software, the components and steps of the various examples have been generally described in terms of functionality in the foregoing description. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0233] The steps of the methods or algorithms described in conjunction with the embodiments disclosed herein can be implemented directly by hardware, a software module executed by a processor, or a combination of both. The software module can be located in random access memory (RAM), main memory, read-only memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, hard disk, removable disk, CD-ROM, or any other form of storage medium known in the art.

[0234] Finally, it should be noted that in this document, relationships such as "first" and "second" are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "include," "contain," or any other variations are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus.

[0235] This document uses specific examples to illustrate the principles and implementation methods of this application. The descriptions of the above embodiments are only for the purpose of helping to understand the methods and core ideas of this application. At the same time, for those skilled in the art, there will be changes in the specific implementation methods and application scope based on the ideas of this application. Therefore, the content of this specification should not be construed as a limitation of this application.

Claims

1. A method for enhancing the image compression process, characterized in that, include: Based on the filtering of the original ultrasound image with high dynamic range, the basic layer to be processed is obtained. According to the different grayscale mapping relationships corresponding to different adjustment parameters, the base layer to be processed is compressed and mapped to obtain multiple different low dynamic range detail enhancement base layers. Calculate the image information entropy of each detail enhancement base layer, and determine a reference base layer from the detail enhancement base layers based on the calculated image information entropy; Based on the feature map of the reference base layer, the pixels of the reference base layer are optimized to obtain the target base layer. Based on the target base layer, a target ultrasound image is obtained; the target ultrasound image is a low dynamic range image.

2. The method according to claim 1, characterized in that, Based on the target base layer, a target ultrasound image is obtained, including: Obtain the detail layer of the original ultrasound image; Pixels in the detail layer whose amplitude exceeds the upper limit of the preset amplitude range are compressed and restricted, and pixels in the detail layer whose amplitude is lower than the lower limit of the preset amplitude range are stretched and enlarged to obtain an enhanced detail layer; The target base layer and the enhanced detail layer are fused to obtain the target ultrasound image.

3. The method according to claim 2, characterized in that, The target base layer and the enhanced detail layer are fused to obtain the target ultrasound image, including: Noise suppression is applied to the enhanced detail layer; The target base layer is fused with the noise-suppressed enhanced detail layer to obtain the target ultrasound image.

4. The method according to claim 3, characterized in that, Noise suppression is performed on the enhanced detail layer, including: Take all pixels in the enhanced detail layer as the first pixel and perform the following operations: A first window is defined in the enhanced detail layer, centered on the first pixel in the enhanced detail layer; In the first window, identify a second pixel that is different from the first pixel; A second window is defined in the enhanced detail layer, centered on the second pixel. Calculate the correlation between the first window and the second window to obtain the correlation value between the first pixel and the second pixel; Calculate the noise suppression factor based on the correlation value; Based on the noise suppression factor, the pixel value of the first pixel is updated; After all pixels have undergone the above operations, the enhanced detail layer with noise suppression is obtained.

5. The method according to claim 2, characterized in that, The target base layer and the enhanced detail layer are fused to obtain the target ultrasound image, including: Feature information is extracted from the target base layer, and adaptive weights are determined using the feature information; The target base layer and the enhanced detail layer are fused using the adaptive weights to obtain the target ultrasound image.

6. The method according to claim 5, characterized in that, Extracting feature information from the target base layer and using the feature information to determine adaptive weights includes: Based on the feature extraction function, the pixel value corresponding to any pixel in the target base layer is compared with the reference pixel value, and the pixel feature value is extracted from the target base layer based on the comparison result to obtain the feature information; wherein, the reference pixel value is a pixel value calculated based on the maximum pixel value in the target base layer; The adaptive weights are calculated based on the maximum and minimum values ​​corresponding to the feature information.

7. The method according to any one of claims 1 to 6, characterized in that, Based on filtering of the original ultrasound image with high dynamic range, a basic layer to be processed is obtained, including: The original ultrasound image is subjected to multi-scale filtering to obtain initial basic layers corresponding to different scales; Subtract the initial base layer corresponding to different scales from the original ultrasound image to obtain the initial detail layer corresponding to different scales. The initial detail layers at different scales are weighted and fused to obtain the reference detail layer; The reference detail layer is subtracted from the original ultrasound image to obtain the base layer to be processed.

8. The method according to any one of claims 1 to 6, characterized in that, Determining a reference base layer from the detail enhancement base layer based on the calculated image information entropy includes: The maximum image information entropy is determined from the calculated image information entropy; The detail enhancement base layer corresponding to the maximum image information entropy is determined as the reference base layer.

9. The method according to any one of claims 1 to 6, characterized in that, The feature map is a gray-level feature histogram; Based on the feature map of the reference base layer, the pixels of the reference base layer are optimized to obtain the target base layer, including: The initial gray-level feature histogram of the reference base layer is subjected to equalization processing to obtain an equalized gray-level feature histogram. The initial gray-level feature histogram is corrected and integrated based on the initial gray-level feature histogram and the balanced gray-level feature histogram to obtain the corrected gray-level feature histogram. Based on the corrected grayscale feature histogram, the reference base layer is modified to optimize the pixels of the reference base layer, thereby obtaining the target base layer.

10. The method according to any one of claims 1 to 6, characterized in that, Based on the different grayscale mapping relationships corresponding to different adjustment parameters, the base layer to be processed is subjected to compression mapping processing to obtain multiple different low dynamic range detail enhancement base layers, including: According to different adjustment parameters, the gray-level feature histogram of the base layer to be processed is modified to obtain several modified gray-level feature histograms. Based on the gray-level mapping relationship corresponding to different modified gray-level feature histograms, the base layer to be processed is subjected to compression mapping processing to obtain multiple different low dynamic range detail enhancement base layers.

11. An enhancement device for an image compression process, characterized in that, include: The filtering module is used to obtain the basic layer to be processed based on the filtering processing of the original ultrasound image with high dynamic range; The compression module is used to perform compression mapping processing on the base layer to be processed according to different grayscale mapping relationships corresponding to different adjustment parameters, so as to obtain multiple different low dynamic range detail enhancement base layers. A filtering module is used to calculate the image information entropy of each detail enhancement base layer and determine a reference base layer from the detail enhancement base layers based on the calculated image information entropy; An optimization module is used to optimize the pixels of the reference base layer based on the feature map of the reference base layer to obtain the target base layer. The output module is used to obtain a target ultrasound image based on the target base layer; the target ultrasound image is a low dynamic range image.

12. An electronic device, characterized in that, include: Memory, used to store computer programs; A processor, configured to implement the steps of the enhanced method for the image compression process as described in any one of claims 1 to 10 when executing the computer program.

13. A readable storage medium, characterized in that, The readable storage medium stores a computer program that, when executed by a processor, implements the steps of the enhancement method for the image compression process as described in any one of claims 1 to 10.

Images