SAR (Synthetic Aperture Radar) image enhancement method based on region self-adaption and related equipment thereof

Abstract

The invention relates to an SAR (Synthetic Aperture Radar) image enhancement method based on region self-adaption and related equipment thereof. The method comprises the following steps: performing discrete wavelet transform processing on an initial ocean SAR image to generate an initial multi-scale wavelet coefficient; establishing an initial universal hidden Markov tree model and determining a high-noise region image and a low-noise region image; performing discrete wavelet transform processing on the high-noise region image and the low-noise region image to generate a high-noise multi-scale wavelet coefficient and a low-noise multi-scale wavelet coefficient; respectively establishing a high-noise universal hidden Markov tree model and a low-noise universal hidden Markov tree model; determining a high-noise region wavelet coefficient and a low-noise region wavelet coefficient; updating the initial universal hidden Markov tree model to obtain a target universal hidden Markov tree model; and performing wavelet inverse transformation on the wavelet coefficient in the target general hidden Markov tree model to obtain a target ocean SAR image.

Description

Technical Field

[0001] The present application relates to the field of computer technology, and in particular to a SAR image enhancement method based on regional adaptation and related equipment. Background Art

[0002] Marine SAR images are acquired through synthetic aperture radar and can provide high-resolution images of the ocean surface under various meteorological conditions. However, these images are usually interfered by noise and the features are not clear enough, so enhancement technology is needed to reduce noise, enhance image features, improve visual effects, and increase data processing efficiency, so as to more accurately support ocean monitoring and analysis.

[0003] Existing image enhancement technologies include local filtering, non-local filtering, transform domain filtering, and machine / deep learning-based algorithms. Local filtering (such as mean and median filtering) does not retain enough details of complex images, and non-local filtering has high computational cost when processing large-scale images. Transform domain filtering (such as frequency domain bandpass filtering) is excellent in detail preservation, but has limited processing capabilities for large-scale images and non-uniform noise. The effect of machine / deep learning methods in marine SAR image processing is not ideal. In general, existing technologies are inefficient and insufficient in processing large-scale and complex background marine SAR images. Summary of the invention

[0004] The embodiments of the present invention provide a method for SAR image enhancement based on regional adaptation and related devices, which at least solve the problems of low efficiency and insufficient capability in the related art when processing large-scale and complex background ocean SAR images.

[0005] According to a first aspect of an embodiment of the present invention, a SAR image enhancement method based on regional adaptation is provided, comprising:

[0006] Performing discrete wavelet transform processing on the initial ocean SAR image to generate initial multi-scale wavelet coefficients corresponding to the initial ocean SAR image;

[0007] Based on the initial multi-scale wavelet coefficients, an initial general hidden Markov tree model corresponding to the initial ocean SAR image is established, and a high-noise area image and a low-noise area image of the initial ocean SAR image are determined, wherein the initial general hidden Markov tree model is used to determine an implicit state probability set of the initial ocean SAR image;

[0008] Performing discrete wavelet transform processing on the high-noise region image and the low-noise region image respectively to generate high-noise multiscale wavelet coefficients corresponding to the high-noise region image and low-noise multiscale wavelet coefficients corresponding to the low-noise region image;

[0009] Based on the high-noise multiscale wavelet coefficients and the low-noise multiscale wavelet coefficients, respectively establishing a high-noise universal hidden Markov tree model corresponding to the high-noise region image and a low-noise universal hidden Markov tree model corresponding to the low-noise region image, the high-noise universal hidden Markov tree model is used to determine the hidden state probability set of the high-noise region image, and the low-noise universal hidden Markov tree model is used to determine the hidden state probability set of the low-noise region image;

[0010] Determining the wavelet coefficients of the high noise region according to the implicit state probability set of the high noise region image, and determining the wavelet coefficients of the low noise region according to the implicit state probability set of the low noise region image;

[0011] Based on the high-noise region wavelet coefficients and the low-noise region wavelet coefficients, the initial multi-scale wavelet coefficients corresponding to the high-noise region image and the low-noise region image in the initial universal hidden Markov tree model are updated to obtain an updated target universal hidden Markov tree model;

[0012] The wavelet coefficients in the target universal hidden Markov tree model are subjected to inverse wavelet transform to obtain the target ocean SAR image after image enhancement.

[0013] According to a second aspect of an embodiment of the present invention, a SAR image enhancement device based on regional adaptation is provided, comprising:

[0014] A generating module, used for performing discrete wavelet transform processing on the initial ocean SAR image to generate initial multi-scale wavelet coefficients corresponding to the initial ocean SAR image;

[0015] A processing module, used for establishing an initial general hidden Markov tree model corresponding to the initial ocean SAR image based on the initial multi-scale wavelet coefficients and determining a high-noise area image and a low-noise area image of the initial ocean SAR image, wherein the initial general hidden Markov tree model is used for determining an implicit state probability set of the initial ocean SAR image;

[0016] The generating module is further used to perform discrete wavelet transform processing on the high-noise region image and the low-noise region image respectively, to generate high-noise multi-scale wavelet coefficients corresponding to the high-noise region image and low-noise multi-scale wavelet coefficients corresponding to the low-noise region image;

[0017] An establishment module is used to establish a high-noise universal hidden Markov tree model corresponding to the high-noise region image and a low-noise universal hidden Markov tree model corresponding to the low-noise region image based on the high-noise multiscale wavelet coefficients and the low-noise multiscale wavelet coefficients, respectively, wherein the high-noise universal hidden Markov tree model is used to determine the hidden state probability set of the high-noise region image, and the low-noise universal hidden Markov tree model is used to determine the hidden state probability set of the low-noise region image;

[0018] A determination module, used to determine the wavelet coefficients of the high noise region according to the implicit state probability set of the high noise region image, and to determine the wavelet coefficients of the low noise region according to the implicit state probability set of the low noise region image;

[0019] An updating module, configured to update the initial multi-scale wavelet coefficients corresponding to the high-noise region image and the low-noise region image in the initial universal hidden Markov tree model based on the high-noise region wavelet coefficients and the low-noise region wavelet coefficients, to obtain an updated target universal hidden Markov tree model;

[0020] The transformation module is used to perform inverse wavelet transformation on the wavelet coefficients in the target universal hidden Markov tree model to obtain the target ocean SAR image after image enhancement.

[0021] According to a third aspect of an embodiment of the present invention, there is provided an electronic device, comprising: a processor, and a memory storing a program, wherein the program comprises instructions, which, when executed by the processor, cause the processor to execute the method according to the first aspect.

[0022] According to a fourth aspect of an embodiment of the present invention, a non-transitory machine-readable medium storing computer instructions is provided, wherein the computer instructions are used to cause the computer to execute the method according to the first aspect.

[0023] The SAR image enhancement method based on regional adaptation provided by the embodiment of the present invention can improve the efficiency and effect when processing large-scale and complex background ocean SAR images. First, the initial ocean SAR image is subjected to discrete wavelet transform to generate initial multi-scale wavelet coefficients. Then, based on these coefficients, an initial universal hidden Markov tree (uHMT) model is established to identify high-noise and low-noise areas in the image, and a universal hidden Markov tree model is established for each area. Further wavelet transform is performed in these areas to obtain multi-scale wavelet coefficients of high-noise and low-noise areas. Through these coefficients, the coefficients corresponding to these areas in the initial uHMT model are updated, and the enhanced wavelet coefficients of the high-noise and low-noise areas are calculated, and the updated uHMT model is subjected to inverse wavelet transform to obtain the enhanced target SAR image. This method can adaptively adjust the processing strategy, optimize the enhancement effect for areas with different noise levels, thereby improving the clarity and detail retention of the image, and is particularly suitable for processing large-scale and complex background ocean SAR images. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the prior art descriptions. Obviously, the drawings described below are only some embodiments of the present invention, and for ordinary technicians in this field, other embodiments can be obtained based on these drawings without creative work.

[0025] Figure 1 A schematic flow chart of a SAR image enhancement method based on regional adaptation provided by an embodiment of the present invention.

[0026] Figure 2 A schematic diagram of an initial ocean SAR image provided by an embodiment of the present invention.

[0027] Figure 3 A schematic diagram of a target ocean SAR image provided by an embodiment of the present invention.

[0028] Figure 4 A schematic structural diagram of a SAR image enhancement device based on regional adaptation provided by an embodiment of the present invention.

[0029] Figure 5 A schematic diagram of the structure of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0030] Embodiments of the present embodiment will be described in more detail below with reference to the accompanying drawings. Although certain embodiments of the present embodiment are shown in the accompanying drawings, it should be understood that the present embodiment can be implemented in various forms and should not be construed as being limited to the embodiments set forth herein, which are instead provided for a more thorough and complete understanding of the present embodiment. It should be understood that the drawings and embodiments of the present embodiment are only for exemplary purposes and are not intended to limit the scope of protection of the present embodiment.

[0031] Marine synthetic aperture radar (SAR) images are acquired through synthetic aperture radar and can provide high-resolution images of the ocean surface under various meteorological conditions, making it an important tool for ocean monitoring and analysis. However, despite these advantages, SAR images are often interfered by noise, and their detailed features are often not clear enough. These problems make the interpretation and analysis of images difficult, affecting the accuracy of marine environmental monitoring. Therefore, image enhancement technology plays a particularly critical role in marine SAR image processing, with the goal of reducing noise, enhancing image details, improving visual effects, and improving data processing efficiency, so as to more accurately support ocean monitoring and analysis tasks.

[0032] At present, commonly used image enhancement techniques include local filtering, non-local filtering, transform domain filtering, and algorithms based on machine learning and deep learning. However, each of these techniques has its limitations. Although local filtering methods (such as mean filtering and median filtering) can reduce noise to a certain extent, they perform poorly in retaining complex image details and often result in blurred images. Non-local filtering methods can better preserve image details, but their computational cost is extremely high when processing large-scale images, which limits their efficiency in practical applications. Transform domain filtering (such as frequency domain bandpass filtering) performs well in retaining details, but its processing capabilities for large-scale images and non-uniform noise are limited, resulting in limited application effects in various complex marine environments. Although methods based on machine learning and deep learning have shown great potential in the field of image processing, their effects are still unstable in marine SAR image enhancement and require a large amount of labeled data and computing resources.

[0033] In general, existing image enhancement technologies are inefficient and limited in capabilities when facing large-scale, complex background ocean SAR images. The shortcomings of these technologies not only affect the clarity and detail presentation of SAR images, but also limit the accuracy and reliability of ocean monitoring and analysis. Therefore, developing more efficient and accurate ocean SAR image enhancement technologies to provide high-quality image data in various complex environments has become a challenge that needs to be addressed.

[0034] In order to solve the above problems, an embodiment of the present invention provides a SAR image enhancement method based on regional adaptation and related devices thereof.

[0035] Figure 1 A schematic flow chart of a SAR image enhancement method based on regional adaptation provided by an embodiment of the present invention. Figure 1 As shown, the method includes the following steps.

[0036] Step S101 , performing discrete wavelet transform processing on the initial ocean SAR image to generate initial multi-scale wavelet coefficients corresponding to the initial ocean SAR image.

[0037] Step S102, based on the initial multi-scale wavelet coefficients, an initial general hidden Markov tree model corresponding to the initial ocean SAR image is established and a high-noise area image and a low-noise area image of the initial ocean SAR image are determined. The initial general hidden Markov tree model is used to determine the implicit state probability set of the initial ocean SAR image.

[0038] Step S103 , performing discrete wavelet transform processing on the high noise region image and the low noise region image respectively, to generate high noise multiscale wavelet coefficients corresponding to the high noise region image and low noise multiscale wavelet coefficients corresponding to the low noise region image.

[0039] Step S104, based on the high-noise multi-scale wavelet coefficients and the low-noise multi-scale wavelet coefficients, respectively establish a high-noise universal hidden Markov tree model corresponding to the high-noise area image and a low-noise universal hidden Markov tree model corresponding to the low-noise area image, the high-noise universal hidden Markov tree model is used to determine the implicit state probability set of the high-noise area image, and the low-noise universal hidden Markov tree model is used to determine the implicit state probability set of the low-noise area image.

[0040] Step S105, determining the wavelet coefficients of the high noise region according to the implicit state probability set of the high noise region image, and determining the wavelet coefficients of the low noise region according to the implicit state probability set of the low noise region image.

[0041] Step S106, based on the high noise region wavelet coefficients and the low noise region wavelet coefficients, the initial multi-scale wavelet coefficients corresponding to the high noise region image and the low noise region image in the initial universal hidden Markov tree model are updated to obtain an updated target universal hidden Markov tree model.

[0042] Step S107, performing inverse wavelet transform on the wavelet coefficients in the target general hidden Markov tree model to obtain the target ocean SAR image after image enhancement.

[0043] Firstly, the initial ocean SAR image is processed by discrete wavelet transform to generate initial multi-scale wavelet coefficients corresponding to the initial ocean SAR image.

[0044] In this embodiment, the initial ocean SAR image is first processed by discrete wavelet transform. Discrete wavelet transform is a technology that captures image information in the space and frequency domain by decomposing different frequency components of the signal. It decomposes the initial ocean SAR image into high-frequency and low-frequency components of different scales through the combined operation of high-pass and low-pass filters. This decomposition can effectively capture the edge and texture features in the initial ocean SAR image.

[0045] Specifically, high-frequency components are used to represent image details, such as edges and textures. These high-frequency components are usually more susceptible to noise. Low-frequency components are used to represent global information and relatively smooth areas of the image.

[0046] In this embodiment, by processing the initial ocean SAR image, the generated initial multi-scale wavelet coefficients contain the above-mentioned high-frequency and low-frequency features, and are used in the subsequent noise detection and processing process.

[0047] After generating the initial multiscale wavelet coefficients corresponding to the initial ocean SAR image, an initial general hidden Markov tree model corresponding to the initial ocean SAR image is established based on the initial multiscale wavelet coefficients, and a high noise region image and a low noise region image of the initial ocean SAR image are determined. In this embodiment, the initial general hidden Markov tree model is used to determine the implicit state probability set of the initial ocean SAR image.

[0048] In this embodiment, an initial universal hidden Markov tree model can be established based on the initial multi-scale wavelet coefficients generated in the above steps. The initial universal hidden Markov tree model is a statistical model that models data dependencies at different scales, which can effectively process the multi-scale features of images and can capture the dependencies between different scales.

[0049] Specifically, the general hidden Markov tree model can regard the wavelet coefficients in the initial ocean SAR image as a set of random variables, and describe the dependencies between these variables through the state transition relationship between parent and child nodes. It can capture the probabilistic relationship between multiple scales.

[0050] In addition, by analyzing the statistical characteristics of the initial multi-scale wavelet coefficients, the initial general hidden Markov tree model can separate the high noise area and the low noise area in the image. In practical applications, the amplitude and scale of the wavelet coefficients can be used to distinguish the areas with higher noise.

[0051] Afterwards, discrete wavelet transform is performed on the high-noise region image and the low-noise region image respectively to generate high-noise multi-scale wavelet coefficients corresponding to the high-noise region image and low-noise multi-scale wavelet coefficients corresponding to the low-noise region image.

[0052] In this embodiment, the high-noise and low-noise area images identified in the above steps are respectively subjected to new discrete wavelet transform processing. In these high-noise area images, there may be more noise components, and the discrete wavelet transform will capture the high-frequency wavelet coefficients of these areas to form high-noise multi-scale wavelet coefficients. The low-noise area images are relatively stable and contain less noise. The discrete wavelet transform captures relatively smooth wavelet coefficients to form low-noise multi-scale wavelet coefficients.

[0053] After obtaining the high-noise multiscale wavelet coefficients corresponding to the high-noise region image and the low-noise multiscale wavelet coefficients corresponding to the low-noise region image, a high-noise universal hidden Markov tree model corresponding to the high-noise region image and a low-noise universal hidden Markov tree model corresponding to the low-noise region image can be established based on the high-noise multiscale wavelet coefficients and the low-noise multiscale wavelet coefficients. In this embodiment, the high-noise universal hidden Markov tree model is used to determine the implicit state probability set of the high-noise region image, and the low-noise universal hidden Markov tree model is used to determine the implicit state probability set of the low-noise region image.

[0054] In this embodiment, based on the initial general hidden Markov tree model, hidden Markov tree models are separately constructed for different noise feature areas.

[0055] Specifically, the high-noise universal hidden Markov tree model is established based on the high-noise multi-scale wavelet coefficients, which is used to describe the statistical characteristics in the high-noise area and capture the probabilistic relationship between the wavelet coefficients in these areas. The low-noise universal hidden Markov tree model is established based on the low-noise multi-scale wavelet coefficients, which is used to describe the relatively stable statistical characteristics of the low-noise area. By establishing high-noise and low-noise models respectively, subsequent image processing can be performed more accurately.

[0056] Afterwards, the wavelet coefficients of the high noise region are determined according to the implicit state probability set of the high noise region image, and the wavelet coefficients of the low noise region are determined according to the implicit state probability set of the low noise region image.

[0057] In this embodiment, by using the implicit state probability set calculated in the hidden Markov tree model, new wavelet coefficients in the high noise area and the low noise area are estimated. The implicit state probability set is obtained through model training and can represent the probability distribution of wavelet coefficients under different noise conditions. According to the above implicit state probability set, wavelet coefficients closer to the true value can be estimated to remove the influence of noise on the coefficients.

[0058] The initial multi-scale wavelet coefficients corresponding to the high-noise region image and the low-noise region image in the initial universal hidden Markov tree model are updated based on the high-noise region wavelet coefficients and the low-noise region wavelet coefficients to obtain an updated target universal hidden Markov tree model.

[0059] In this embodiment, the updated wavelet coefficients estimated from the high noise and low noise regions are used to adjust the initial hidden Markov tree model. Through the above feedback update process, the accuracy of the model is improved by replacing the original coefficients with more accurate coefficients. The initial general hidden Markov tree model becomes more accurate through the above steps, reflecting the real characteristics of different regions in the current SAR image.

[0060] Finally, the wavelet coefficients in the target general hidden Markov tree model are inversely transformed to obtain the target ocean SAR image after image enhancement.

[0061] In this embodiment, the wavelet coefficients in the updated and optimized hidden Markov tree model are subjected to inverse wavelet transform. The multi-scale wavelet coefficients are recombined into an image to generate the final enhanced ocean SAR image. Specifically, inverse wavelet transform is a process of reconstructing the image data after wavelet decomposition back to the original image space.

[0062] Based on the above method, the multi-scale characteristics of discrete wavelet transform and hidden Markov tree model can be effectively used to enhance SAR images. From the decomposition of the initial image, to the modeling of regional noise characteristics, to the update and inverse transformation processing, the image enhancement and noise suppression are finally achieved. It can be applied to the processing of SAR images with large noise, making the target information in the image easier to analyze and identify.

[0063] In an optional embodiment, the initial multi-scale wavelet coefficients include a first high-frequency characteristic wavelet coefficient and a first low-frequency characteristic wavelet coefficient, the high-noise multi-scale wavelet coefficients include a second high-frequency characteristic wavelet coefficient and a second low-frequency characteristic wavelet coefficient, and the low-noise multi-scale wavelet coefficients include a third high-frequency characteristic wavelet coefficient and a third low-frequency characteristic wavelet coefficient.

[0064] In this embodiment, when performing discrete wavelet transform processing on the initial ocean SAR image to generate initial multi-scale wavelet coefficients corresponding to the initial ocean SAR image, the initial ocean SAR image can be processed by a high-pass filter and a low-pass filter based on the Haar wavelet basis function to generate a first high-frequency characteristic wavelet coefficient and a first low-frequency characteristic wavelet coefficient.

[0065] The Haar wavelet basis function defines a specific filter structure, which decomposes and reconstructs the image through a pair of mother wavelets and a scaling function. In SAR image processing applications, the Haar wavelet can quickly decompose the image at multiple scales due to its simple computational characteristics, making it suitable for real-time or near-real-time image enhancement processing.

[0066] In the wavelet transform process, the image is decomposed through a set of filters. For the Haar wavelet, these filters include a high-pass filter and a low-pass filter. The application of these filters generates different frequency components of the image, allowing the image's features to be analyzed at different scales:

[0067] Low-pass filter: used to capture low-frequency information (i.e. smooth parts or general structures) in an image and generate low-frequency feature wavelet coefficients. Low-frequency information usually retains the main structure of the image and has a higher energy density. The output of the low-pass filter represents a rough version of the image and captures large-scale image features.

[0068] High-pass filter: It is used to extract high-frequency information (i.e. edges and details) of an image and generate high-frequency feature wavelet coefficients. High-frequency information usually contains detailed features such as texture, edges, and noise of the image. The output of the high-pass filter provides detailed information of the image, which can be used for further feature analysis.

[0069] After applying the high-pass filter and the low-pass filter, a first high-frequency characteristic wavelet coefficient and a first low-frequency characteristic wavelet coefficient are obtained.

[0070] Among them, the first high-frequency feature wavelet coefficient is used to represent the detailed features of the image, especially the edge and texture information. It is captured by a high-pass filter and is significant in the high-noise area or edge features of the image, and can be used to enhance the details and sharpness of the image. The first low-frequency feature wavelet coefficient is used to represent the low-frequency information of the image, representing the basic structure and smooth part of the image. The low-frequency coefficient is obtained by a low-pass filter, which retains the main energy and overall shape of the image. Low-frequency information is mainly used to maintain the basic form of the image and reduce noise.

[0071] After performing the wavelet decomposition, the first high-frequency characteristic wavelet coefficient and the first low-frequency characteristic wavelet coefficient are stored for use in subsequent steps. These coefficients can be stored in the form of a matrix or a multidimensional array for further processing and calculation. For example, in a hidden Markov tree model, these coefficients will be used to build an initial model and further determine the hidden state probability set of the image.

[0072] The initial ocean SAR image is processed by discrete wavelet transform using Haar wavelet basis functions, which can capture different frequency features in the image. The application of low-pass and high-pass filters enables the image to be effectively decomposed at different scales, thereby extracting the high-frequency and low-frequency features of the image.

[0073] In an optional embodiment, an initial general hidden Markov tree model corresponding to the initial ocean SAR image may be established based on the initial multi-scale wavelet coefficients.

[0074] Specifically, the first probability density function of the mixed Gaussian model of each initial multi-scale wavelet coefficient can be determined, and the first node relationship between different scale layers of each initial multi-scale wavelet coefficient can be determined through the parameter set of the hidden Markov tree model. Then, the initial general hidden Markov tree model is established based on the first probability density function and the first node relationship.

[0075] In this embodiment, a mixed Gaussian model can first be constructed using the initial multi-scale wavelet coefficients. A mixed Gaussian model is a probabilistic model that assumes that a data point is composed of multiple Gaussian distributions (usually referred to as components). Each of these Gaussian components has its own mean, variance, and weight. The following is a specific implementation method for this step:

[0076] Acquisition of multi-scale wavelet coefficients: The initial multi-scale wavelet coefficients are generated from the initial ocean SAR image through discrete wavelet transform. These initial multi-scale wavelet coefficients reflect the characteristic information of the image at different scales.

[0077] Establish a mixed Gaussian model: treat each initial multi-scale wavelet coefficient as a data point, assuming that these data points can be represented by a mixed Gaussian model. Fit a Gaussian model with K components using the expectation maximization (EM) algorithm or other suitable estimation algorithms.

[0078] The first probability density function of the mixed Gaussian model can be determined based on the following formula (1):

[0079]

[0080] Where x is the initial multi-scale wavelet coefficient; p(x) is the first probability density function of x; K is the number of Gaussian distribution components in the mixed Gaussian model; π k is the prior probability of state k, N(x,μ k ,∑ k ) is the probability of x given the kth Gaussian distribution component, μ k is the mean vector of the kth Gaussian distribution, ∑ k is the covariance matrix of the kth Gaussian distribution.

[0081] At the same time, the Hidden Markov Tree (HMT) model is used to capture the dependencies between wavelet coefficients at different scales. The Hidden Markov Tree model is a graphical model that can effectively describe the probabilistic dependencies in a hierarchical structure and is particularly suitable for multi-scale analysis, such as wavelet transform.

[0082] Each node in the hidden Markov tree model corresponds to a wavelet coefficient, and the connections between nodes represent their dependencies at different scales. The root node corresponds to the coefficient of the lowest scale, and the leaf node corresponds to the coefficient of the highest scale. There is a state transition probability between each parent node and its child nodes.

[0083] In this embodiment, the parameter set of the hidden Markov tree model can be determined based on the following formula (2):

[0084]

[0085] Among them, θ is the parameter set of the hidden Markov tree model; p s1 (m) is the state probability distribution function of the root node state s1, which represents the probability of the root node being in the implicit state m; is the state transition probability between parent node i and child node ρ(i), which means the probability that the child node transfers to state n when the parent node is in state m; μ i,m is the mean of the probability density function of the wavelet coefficients of the current node i in the implicit state m; is the variance of the probability density function of the wavelet coefficients of the current node i in the implicit state m.

[0086] Through the parameter set of the hidden Markov tree model, the node relationship between the initial multi-scale wavelet coefficients at different scale levels can be defined, including the state transition probability between parent and child nodes at different levels and the statistical characteristics of the corresponding wavelet coefficients.

[0087] The implicit state probability set of the initial ocean SAR image is determined based on the following method: based on the meta-parameters of the initial general hidden Markov tree model, the implicit state probability set is determined by the maximum expectation algorithm.

[0088] The hidden state probability set is one of the core results of the hidden Markov tree model output, which can be used to represent the probability of signals and noise in different states under each wavelet coefficient.

[0089] In practical applications, the implicit state probability of each wavelet coefficient in the initial ocean SAR image can be calculated by the EM algorithm based on the initial general hidden Markov tree model. The algorithm is iteratively updated until it converges to a stable state.

[0090] In an optional embodiment, the high-noise region image and the low-noise region image of the initial ocean SAR image may be determined based on the initial multi-scale wavelet coefficients.

[0091] In this embodiment, the initial ocean SAR image can be subjected to sliding window processing based on a window of a preset size to determine the median of the first scale HH subband of multiple initial multiscale wavelet coefficients in each window; then the median corresponding to each window is sorted, and the sorting result is output to determine the high noise area image and the low noise area image based on the sorting result.

[0092] In this embodiment, the sliding window processing can analyze the noise characteristics of the image in the local area, so as to effectively distinguish the high noise and low noise areas. By dividing the image into multiple small windows, the noise distribution can be observed at a finer scale.

[0093] Specifically, a fixed-size window (e.g., 64x64, 128x128, or 256x256 pixels) can be defined, and the window is gradually slid from left to right and from top to bottom on the entire initial ocean SAR image. Each time the window slides, it covers a local area in the image. During the sliding process, the position of the window moves gradually, one pixel or more pixels (set according to requirements) each time, until the entire image is covered.

[0094] Through the sliding window operation, the initial image is divided into multiple local areas, each of which is covered by a window. Each window contains several pixel values ​​of the image for subsequent noise analysis.

[0095] In this embodiment, the wavelet coefficients of the first scale HH subband are mainly used to capture high-frequency detail information in the image, which is usually greatly affected by noise. Therefore, extracting the median of the HH subband can help identify the noise intensity.

[0096] Specifically, the initial ocean SAR image is subjected to a multi-scale wavelet transform to obtain the wavelet coefficients of the first scale HH subband. These coefficients reflect the intensity of the details and edge features in the image. For each sliding window area, the wavelet coefficients corresponding to all pixels in the area in the first scale HH subband are extracted. Then the median of these wavelet coefficients is calculated. Median calculation is a robust statistical method that is less susceptible to noise interference and can effectively reflect the typical wavelet coefficient values ​​in the area.

[0097] Each sliding window area corresponds to a median value, which represents the central tendency of high-frequency features in the local area and is used to distinguish noise characteristics.

[0098] In this embodiment, the sorted median can be used to compare the noise levels of various local areas, thereby determining which areas have higher noise characteristics.

[0099] Specifically, the median values ​​of all sliding window areas are collected into a list or array. These median values ​​are then sorted from lowest to highest, or arranged according to a specific sorting rule (such as descending or ascending). Sorting can use common sorting algorithms such as quick sort or merge sort.

[0100] The sorted median sequence can clearly show the noise intensity distribution in different areas of the image. The termination sequence is arranged from large to small, so the values ​​in the front represent areas with lower noise, while the values ​​in the back represent areas with higher noise. In practical applications, the areas represented by the first 5% of the median values ​​can be considered as high-noise areas, and the last 5% as low-noise areas.

[0101] In an optional embodiment, when respectively establishing a high-noise universal hidden Markov tree model corresponding to a high-noise region image and a low-noise universal hidden Markov tree model corresponding to a low-noise region image based on high-noise multiscale wavelet coefficients and low-noise multiscale wavelet coefficients, the following steps may be performed:

[0102] First, the second probability density functions of high-noise multiscale wavelet coefficients and low-noise multiscale wavelet coefficients are determined. Then, the second node relationship between high-noise multiscale wavelet coefficients and low-noise multiscale wavelet coefficients at different scale layers is determined through the parameter set of the hidden Markov tree model. Finally, based on the second probability density function and the second node relationship, a high-noise universal hidden Markov tree model and a low-noise universal hidden Markov tree model are established respectively.

[0103] In this embodiment, during the construction of the general hidden Markov tree model, the probability density function is used to describe the statistical characteristics of the wavelet coefficients. This step can define statistical models for the wavelet coefficients in the high noise region and the low noise region respectively.

[0104] In high-noise areas, since the signal is greatly affected by noise, the distribution characteristics of the wavelet coefficients may be different from those in low-noise areas. By determining the probability density function of the high-noise area, the statistical characteristics of these coefficients can be effectively described. Specifically, the mixed Gaussian model can be used to characterize it, because the mixed Gaussian model can flexibly adapt to the different distribution characteristics of data, especially in complex natural scenes.

[0105] The multi-scale wavelet coefficients in low-noise regions generally show more stable and concentrated distribution characteristics. Statistical modeling of these coefficients can help accurately distinguish between signal and noise during image enhancement.

[0106] In this embodiment, the hidden Markov tree model captures the dependencies between multi-scale wavelet coefficients through the relationship between nodes. The state transfer relationship between nodes can be established by using wavelet coefficients at different scale levels.

[0107] In the hidden Markov tree structure, each wavelet coefficient can be regarded as a node. The relationship between parent nodes and child nodes reflects the information transfer between different scale layers. For example, a parent node at a coarse scale may control the state distribution of several child nodes at a finer scale. By defining these node relationships, the signal characteristics and noise characteristics between multiple scales can be captured in the model.

[0108] The state transition relationship between nodes is usually represented by a transition probability matrix. Each element of the matrix describes the probability of a child node state changing when the parent node is in a certain state. Different hidden Markov tree models have different parameter sets to represent node relationships under different noise characteristics.

[0109] In this embodiment, a high-noise and low-noise universal hidden Markov tree model may be established based on the second probability density function and the second node relationship.

[0110] Specifically, based on the second probability density function and the second node relationship, a hidden Markov tree model specifically for high noise areas is constructed. This model can describe the distribution and scale relationship of multi-scale wavelet coefficients in high noise areas, which helps to effectively distinguish and remove noise during the enhancement process.

[0111] Similarly, based on the second probability density function and the second node relationship in the low noise region, a hidden Markov tree model suitable for the low noise region can also be constructed. Since the signal in the low noise region is more stable, this model can more accurately maintain the detailed characteristics of the signal while reducing the impact of noise.

[0112] The statistical characteristics and node relationships in the high-noise hidden Markov tree model are used to reduce the impact of noise and retain or enhance useful signal information. The low-noise hidden Markov tree model is used to accurately maintain the low-noise detail characteristics of the image and avoid detail loss caused by over-smoothing.

[0113] In an optional embodiment, when the high noise region wavelet coefficients are determined according to the implicit state probability set of the high noise region image, and the low noise region wavelet coefficients are determined according to the implicit state probability set of the low noise region image, the conditional means corresponding to the high noise region image and the low noise region image can be determined based on the following formula (3):

[0114]

[0115] Among them, Y i Represents the wavelet coefficients of the signal in the image; W i Represents the wavelet coefficients of the parent node at different scales; S i represents the implicit state of node i; m represents the specific value of the implicit state; represents the variance of the wavelet coefficients of node i in the hidden state m; σ 2 represents the variance of the noise; ω i Represents the wavelet coefficient of the parent node of node i;

[0116] Based on the implicit state probability set of the high noise region image and the implicit state probability set of the low noise region image, the wavelet coefficients of the high noise region and the wavelet coefficients of the low noise region are determined based on the following formula (4):

[0117]

[0118] in, represents the new wavelet coefficients, y i is the wavelet coefficient representing the signal in the local image (high noise area image or low noise area image).

[0119] The SAR image enhancement method based on regional adaptation provided by the embodiment of the present invention is described below in conjunction with specific embodiments.

[0120] In this embodiment, it is assumed that Figure 2 is the initial ocean SAR image, and image enhancement processing is performed on it.

[0121] First, a discrete wavelet transform (DWT) is performed on the initial ocean SAR image. Specifically, by applying a set of high-pass and low-pass filters, the image is decomposed into high-frequency and low-frequency components of different scales. Among them, the high-frequency components mainly represent the detailed parts of the image, such as edges and textures, which are usually greatly affected by noise. The low-frequency components represent the overall structure and smoother areas of the image, containing less noise.

[0122] Assuming that the initial ocean SAR image size is 512x512 pixels, the image is decomposed into a low-frequency sub-band and three high-frequency sub-bands (horizontal, vertical, and diagonal) through discrete wavelet transform. These sub-bands form a set of initial multi-scale wavelet coefficients, recording the frequency characteristics of the image at different scales.

[0123] Based on the initial multi-scale wavelet coefficients, an initial universal hidden Markov tree (uHMT) model is established. The hidden Markov tree model is a statistical model used to capture the dependencies between multiple scales.

[0124] Each coefficient in the initial multi-scale wavelet coefficients is regarded as a node in the hidden Markov tree model.

[0125] The connection between nodes represents the dependency of wavelet coefficients at different scales, with parent nodes corresponding to low-frequency coefficients and child nodes corresponding to high-frequency coefficients.

[0126] In this embodiment, it is assumed that the initial HMT model may contain a three-layer structure, each layer represents a different scale. The parent node represents the high-scale feature (low-frequency information), while the child node represents the low-scale feature (high-frequency information). By training this model, the state probability distribution of each wavelet coefficient in the image can be obtained, providing a basis for subsequent noise separation and feature enhancement.

[0127] Through the initial universal hidden Markov tree model, the high noise area image and the low noise area image can be determined. Then, these areas are processed again by discrete wavelet transform to generate multi-scale wavelet coefficients of high noise and low noise areas.

[0128] In high-noise areas, the amplitude of the wavelet coefficients is higher, usually representing more details and texture features. In low-noise areas, the amplitude of the wavelet coefficients is lower, representing smoother and more stable image areas.

[0129] For example, for images in high-noise areas, discrete wavelet transform may generate more prominent high-frequency features, which will be used for noise filtering and image sharpening in subsequent steps. Low-noise areas retain their original smooth characteristics, which helps maintain the overall stability of the image.

[0130] Based on the wavelet coefficients of high-noise and low-noise regions, universal hidden Markov tree models for high-noise and low-noise regions are established, respectively. These models are used to capture the probabilistic dependencies between regions with different noise characteristics.

[0131] The high-noise HMT model describes the statistical characteristics of the high-noise region, including the state transition probability between wavelet coefficients. The low-noise HMT model is used to capture the relatively stable characteristics of the low-noise region.

[0132] By analyzing the high-frequency wavelet coefficients in high-noise regions, a high-noise HMT model is established that can effectively identify and filter out the noise in these regions while preserving the details and edges of the image. Similarly, the low-noise HMT model ensures that smooth and continuous image structures are maintained in low-noise regions.

[0133] According to the implicit state probability set of the high noise and low noise area images, the wavelet coefficients of the high noise area and the low noise area are estimated respectively. In this way, the denoised wavelet coefficients can be obtained, thereby reducing the influence of noise on image details.

[0134] The implicit state probability set represents the probability distribution of the wavelet coefficients under different noise conditions, and the initial wavelet coefficients are adjusted through these probability distributions.

[0135] In the high noise region, the high-frequency wavelet coefficients are adjusted using the implicit state probabilities calculated by the high-noise HMT model, making these coefficients closer to the actual image features and reducing the errors introduced by noise. Similarly, the wavelet coefficients in the low noise region are adjusted using the low-noise HMT model to preserve the smoothness of the image.

[0136] The estimated wavelet coefficients of the high-noise and low-noise regions are used to update the initial universal hidden Markov tree model, thereby obtaining a more accurate target universal hidden Markov tree model. The updating process replaces the initial coefficients with the estimated wavelet coefficients to make the model more consistent with the characteristics of the actual image.

[0137] Assuming that the high-frequency coefficients of a node in the original model are greatly affected by noise, the updated wavelet coefficients can more accurately reflect the edge information in the actual image, thereby improving the model's ability to describe image features.

[0138] Finally, an inverse wavelet transform is performed on the wavelet coefficients in the target general hidden Markov tree model to generate an enhanced target ocean SAR image. The inverse wavelet transform process combines the multi-scale wavelet coefficients back into the original image space to reconstruct an image with noise suppression and feature enhancement.

[0139] Figure 3 The target ocean SAR image after image enhancement processing provided by the embodiment of the present invention. Figure 3 As shown in the figure, through the inverse wavelet transform, the information in the model is restored to an enhanced image, the noise is eliminated and the detail clarity of the image is improved. Compared with the initial image, the image obtained after the inverse transform has significantly less noise, sharper edges, and the target object is clearer and easier to identify in the image.

[0140] Based on the regional adaptive SAR image enhancement method provided by the embodiment of the present invention, the embodiment of the present invention also provides a regional adaptive marine synthetic aperture radar image enhancement device. Figure 4 As shown, the device includes a generating module 401 , a processing module 402 , a establishing module 403 , a determining module 404 , an updating module 405 and a transforming module 406 .

[0141] A generating module 401 is used to perform discrete wavelet transform processing on the initial ocean SAR image to generate initial multi-scale wavelet coefficients corresponding to the initial ocean SAR image;

[0142] The processing module 402 is used to establish an initial general hidden Markov tree model corresponding to the initial ocean SAR image based on the initial multi-scale wavelet coefficients and determine the high-noise area image and the low-noise area image of the initial ocean SAR image, and the initial general hidden Markov tree model is used to determine the implicit state probability set of the initial ocean SAR image;

[0143] The generating module 401 is further used to perform discrete wavelet transform processing on the high noise region image and the low noise region image respectively, to generate high noise multi-scale wavelet coefficients corresponding to the high noise region image and low noise multi-scale wavelet coefficients corresponding to the low noise region image;

[0144] Establishing module 403, used for establishing a high-noise universal hidden Markov tree model corresponding to the high-noise region image and a low-noise universal hidden Markov tree model corresponding to the low-noise region image based on the high-noise multiscale wavelet coefficients and the low-noise multiscale wavelet coefficients, respectively, the high-noise universal hidden Markov tree model is used to determine the hidden state probability set of the high-noise region image, and the low-noise universal hidden Markov tree model is used to determine the hidden state probability set of the low-noise region image;

[0145] A determination module 404, configured to determine the wavelet coefficients of the high noise region according to the implicit state probability set of the high noise region image, and to determine the wavelet coefficients of the low noise region according to the implicit state probability set of the low noise region image;

[0146] An updating module 405 is used to update the initial multi-scale wavelet coefficients corresponding to the high-noise region image and the low-noise region image in the initial universal hidden Markov tree model based on the high-noise region wavelet coefficients and the low-noise region wavelet coefficients to obtain an updated target universal hidden Markov tree model;

[0147] The transformation module 406 is used to perform inverse wavelet transformation on the wavelet coefficients in the target general hidden Markov tree model to obtain the target ocean SAR image after image enhancement.

[0148] In this embodiment, the initial multi-scale wavelet coefficients include a first high-frequency characteristic wavelet coefficient and a first low-frequency characteristic wavelet coefficient, the high-noise multi-scale wavelet coefficients include a second high-frequency characteristic wavelet coefficient and a second low-frequency characteristic wavelet coefficient, and the low-noise multi-scale wavelet coefficients include a third high-frequency characteristic wavelet coefficient and a third low-frequency characteristic wavelet coefficient.

[0149] In this embodiment, the generating module 401 is specifically used to process the initial ocean SAR image through a high-pass filter and a low-pass filter respectively based on the Haar wavelet basis function to generate a first high-frequency characteristic wavelet coefficient and a first low-frequency characteristic wavelet coefficient.

[0150] In this embodiment, the processing module 402 is specifically used to determine the first probability density function of the mixed Gaussian model of each initial multi-scale wavelet coefficient, and determine the first node relationship between different scale layers of each initial multi-scale wavelet coefficient through the parameter set of the hidden Markov tree model.

[0151] Establishing an initial universal hidden Markov tree model based on the first probability density function and the first node relationship;

[0152] The first probability density function is determined based on the following formula (1):

[0153]

[0154] Where x is the initial multi-scale wavelet coefficient; p(x) is the first probability density function of x; K is the number of Gaussian distribution components in the mixed Gaussian model; π k is the prior probability of state k, N(x,μ k ,Σ k ) is the probability of x given the kth Gaussian distribution component, μ k is the mean vector of the kth Gaussian distribution, Σ k is the covariance matrix of the kth Gaussian distribution;

[0155] The parameter set of the hidden Markov tree model is determined based on the following formula (2):

[0156]

[0157] Among them, θ is the parameter set of the hidden Markov tree model; p s1 (m) is the state probability distribution function of the root node state s1, which represents the probability of the root node being in the implicit state m; is the state transition probability between parent node i and child node ρ(i), which means the probability that the child node transfers to state n when the parent node is in state m; μ i,m is the mean of the probability density function of the wavelet coefficients of the current node i in the implicit state m; is the variance of the probability density function of the wavelet coefficient of the current node i in the implicit state m;

[0158] The implicit state probability set of the initial ocean SAR image is determined based on the following method:

[0159] Based on the meta-parameters of the initial general hidden Markov tree model, the hidden state probability set is determined by the maximum expectation algorithm.

[0160] In this embodiment, the processing module 402 is specifically used to perform sliding window processing on the initial ocean SAR image based on a window of a preset size, determine the median of the first scale HH subband of multiple initial multi-scale wavelet coefficients in each window; sort the median corresponding to each window, output the sorting result, and determine the high noise area image and the low noise area image based on the sorting result.

[0161] In this embodiment, module 403 is established, which is specifically used to determine the second probability density function of the high-noise multiscale wavelet coefficients and the low-noise multiscale wavelet coefficients; the second node relationship between the high-noise multiscale wavelet coefficients and the low-noise multiscale wavelet coefficients at different scale layers is determined through the parameter set of the hidden Markov tree model; based on the second probability density function and the second node relationship, a high-noise general hidden Markov tree model and a low-noise general hidden Markov tree model are established respectively.

[0162] In this embodiment, the conditional means corresponding to the high noise region image and the low noise region image are determined based on the following formula (3):

[0163]

[0164] Among them, Y i Represents the wavelet coefficients of the signal in the image; W i Represents the wavelet coefficients of the parent node at different scales; S i represents the implicit state of node i; m represents the specific value of the implicit state; represents the variance of the wavelet coefficients of node i in the hidden state m; σ 2 represents the variance of the noise; ω i Represents the wavelet coefficient of the parent node of node i;

[0165] Based on the implicit state probability set of the high noise region image and the implicit state probability set of the low noise region image, the wavelet coefficients of the high noise region and the wavelet coefficients of the low noise region are determined based on the following formula (4):

[0166]

[0167] in, represents the new wavelet coefficients, y i is the wavelet coefficient representing the signal in the local image (high noise area image or low noise area image).

[0168] An embodiment of the present invention further provides an electronic device, comprising: at least one processor; and a memory in communication with the at least one processor. The memory stores a computer program executable by the at least one processor, and when the computer program is executed by the at least one processor, the electronic device executes the method of the embodiment of the present invention.

[0169] An embodiment of the present invention further provides a non-transitory machine-readable medium storing a computer program, wherein the computer program, when executed by a processor of a computer, is used to cause the computer to execute the method of the embodiment of the present invention.

[0170] An embodiment of the present invention further provides a computer program product, including a computer program, wherein the computer program, when executed by a processor of a computer, is used to enable the computer to execute the method of the embodiment of the present invention.

[0171] refer to Figure 5 , a block diagram of an electronic device that can be used as a server or client of an embodiment of the present invention will now be described, which is an example of a hardware device that can be applied to various aspects of the present invention. Electronic devices are intended to represent various forms of digital electronic computer devices, such as laptop computers, desktop computers, workbenches, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. Electronic devices can also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present invention described and / or required herein.

[0172] like Figure 5 As shown, the electronic device includes a computing unit 501, which can perform various appropriate actions and processes according to a computer program stored in a read-only memory (ROM) 502 or a computer program loaded from a storage unit 508 into a random access memory (RAM) 503. In the RAM 503, various programs and data required for the operation of the electronic device can also be stored. The computing unit 501, the ROM 502, and the RAM 503 are connected to each other via a bus 504. An input / output (I / O) interface 505 is also connected to the bus 504.

[0173] Multiple components in the electronic device are connected to the I / O interface 505, including: an input unit 506, an output unit 507, a storage unit 508, and a communication unit 509. The input unit 506 can be any type of device that can input information to the electronic device, and the input unit 506 can receive input digital or character information, and generate key signal input related to user settings and / or function control of the electronic device. The output unit 507 can be any type of device that can present information, and can include but is not limited to a display, a speaker, a video / audio output terminal, a vibrator, and / or a printer. The storage unit 508 can include but is not limited to a disk, an optical disk. The communication unit 509 allows the electronic device to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks, and can include but is not limited to a modem, a network card, an infrared communication device, a wireless communication transceiver, and / or a chipset, such as a Bluetooth device, a WiFi device, a WiMax device, a cellular communication device, and / or the like.

[0174] The computing unit 501 may be a variety of general and / or special processing components with processing and computing capabilities. Some examples of the computing unit 501 include, but are not limited to, a CPU, a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, digital signal processors (DSPs), and any appropriate processors, controllers, microcontrollers, etc. The computing unit 501 performs the various methods and processes described above. For example, in some embodiments, the method embodiments of the present invention may be implemented as a computer program, which is tangibly contained in a machine-readable medium, such as a storage unit 508. In some embodiments, part or all of the computer program may be stored in a ROM.

[0175] 502 and / or the communication unit 509 are loaded and / or installed on the electronic device. In some embodiments, the computing unit 501 can be configured to execute the above method in any other appropriate manner (for example, by means of firmware).

[0176] The computer programs for implementing the methods of the embodiments of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor or controller of a general-purpose computer, a special-purpose computer, or other programmable data processing device, so that when the computer programs are executed by the processor or controller, the functions / operations specified in the flow chart and / or block diagram are implemented. The computer programs may be executed entirely on the machine, partially on the machine, partially on the machine as a stand-alone software package and partially on a remote machine, or entirely on a remote machine or server.

[0177] In the context of an embodiment of the present invention, a machine-readable medium may be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, device, or equipment. A machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable signal medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or equipment, or any suitable combination of the foregoing. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0178] It should be noted that the term "including" and its variations used in the embodiments of the present invention are open inclusions, that is, "including but not limited to". The term "based on" means "based at least in part on". The term "one embodiment" means "at least one embodiment"; the term "another embodiment" means "at least one other embodiment"; the term "some embodiments" means "at least some embodiments". The modifications of "one" and "multiple" mentioned in the embodiments of the present invention are illustrative rather than restrictive. Those skilled in the art should understand that unless the context clearly indicates otherwise, it should be understood as "one or more".

[0179] 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 the embodiments of the present invention are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with relevant laws, regulations and standards of relevant countries and regions, and provide corresponding operation entrances for users to choose to authorize or refuse.

[0180] The various steps described in the method implementation methods provided in the embodiments of the present invention may be performed in different orders and / or in parallel. In addition, the method implementation methods may include additional steps and / or omit the steps shown. The scope of protection of the present invention is not limited in this respect.

[0181] The term "embodiment" in this specification refers to specific features, structures or characteristics described in conjunction with the embodiment that can be included in at least one embodiment of the present invention. The appearance of this phrase in various places in the specification does not necessarily mean the same embodiment, nor does it mean that it is mutually exclusive with other embodiments and is independent or optional. The various embodiments in this specification are described in a related manner, and the same or similar parts between the various embodiments refer to each other. In particular, for the device, equipment, and system embodiments, since they are basically similar to the method embodiments, the description is relatively simple, and the relevant parts refer to the partial description of the method embodiment.

[0182] The above-mentioned embodiments only express several implementation methods of the present invention, and the description thereof is relatively specific and detailed, but it cannot be understood as limiting the scope of patent protection. 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 invention, and these all belong to the scope of protection of the present invention. Therefore, the scope of protection of the present invention shall be subject to the attached claims.

Claims

1. A SAR image enhancement method based on regional adaptation, characterized in that: include: Performing discrete wavelet transform processing on the initial ocean SAR image to generate initial multi-scale wavelet coefficients corresponding to the initial ocean SAR image; Based on the initial multi-scale wavelet coefficients, an initial general hidden Markov tree model corresponding to the initial ocean SAR image is established, and a high-noise area image and a low-noise area image of the initial ocean SAR image are determined, wherein the initial general hidden Markov tree model is used to determine an implicit state probability set of the initial ocean SAR image; Performing discrete wavelet transform processing on the high-noise region image and the low-noise region image respectively to generate high-noise multiscale wavelet coefficients corresponding to the high-noise region image and low-noise multiscale wavelet coefficients corresponding to the low-noise region image; Based on the high-noise multiscale wavelet coefficients and the low-noise multiscale wavelet coefficients, respectively establishing a high-noise universal hidden Markov tree model corresponding to the high-noise region image and a low-noise universal hidden Markov tree model corresponding to the low-noise region image, the high-noise universal hidden Markov tree model is used to determine the hidden state probability set of the high-noise region image, and the low-noise universal hidden Markov tree model is used to determine the hidden state probability set of the low-noise region image; Determining the wavelet coefficients of the high noise region according to the implicit state probability set of the high noise region image, and determining the wavelet coefficients of the low noise region according to the implicit state probability set of the low noise region image; Based on the high-noise region wavelet coefficients and the low-noise region wavelet coefficients, the initial multi-scale wavelet coefficients corresponding to the high-noise region image and the low-noise region image in the initial universal hidden Markov tree model are updated to obtain an updated target universal hidden Markov tree model; The wavelet coefficients in the target universal hidden Markov tree model are subjected to inverse wavelet transform to obtain the target ocean SAR image after image enhancement.

2. The method according to claim 1, characterized in that: The initial multi-scale wavelet coefficients include a first high-frequency characteristic wavelet coefficient and a first low-frequency characteristic wavelet coefficient, the high-noise multi-scale wavelet coefficients include a second high-frequency characteristic wavelet coefficient and a second low-frequency characteristic wavelet coefficient, and the low-noise multi-scale wavelet coefficients include a third high-frequency characteristic wavelet coefficient and a third low-frequency characteristic wavelet coefficient.

3. The method according to claim 2, characterized in that The step of performing discrete wavelet transform processing on the initial ocean SAR image to generate initial multi-scale wavelet coefficients corresponding to the initial ocean SAR image includes: Based on the Haar wavelet basis function, the initial ocean SAR image is processed by a high-pass filter and a low-pass filter respectively to generate the first high-frequency characteristic wavelet coefficient and the first low-frequency characteristic wavelet coefficient.

4. The method according to claim 2, characterized in that: The method of establishing an initial general hidden Markov tree model corresponding to the initial ocean SAR image based on the initial multi-scale wavelet coefficients and determining a high-noise area image and a low-noise area image of the initial ocean SAR image comprises: Determine a first probability density function of a mixed Gaussian model of each of the initial multi-scale wavelet coefficients, and determine a first node relationship between different scale layers of each of the initial multi-scale wavelet coefficients through a parameter set of a hidden Markov tree model; Establishing the initial universal hidden Markov tree model based on the first probability density function and the first node relationship; The first probability density function is determined based on the following formula (1): Where x is the initial multi-scale wavelet coefficient; p(x) is the first probability density function of x; K is the number of Gaussian distribution components in the mixed Gaussian model; π k is the prior probability of state k, N(x,μ k ,Σ k ) is the probability of x given the kth Gaussian distribution component, μ k is the mean vector of the kth Gaussian distribution, Σ k is the covariance matrix of the kth Gaussian distribution; The parameter set of the hidden Markov tree model is determined based on the following formula (2): Among them, θ is the parameter set of the hidden Markov tree model; p s1 () is the state probability distribution function of the root node state s1, which represents the probability of the root node being in the implicit state m; is the state transition probability between parent node i and child node ρ(i), which means the probability that the child node transfers to state n when the parent node is in state m; μ i,m is the mean of the probability density function of the wavelet coefficients of the current node i in the implicit state m; is the variance of the probability density function of the wavelet coefficient of the current node i in the implicit state m; The implicit state probability set of the initial ocean SAR image is determined based on the following method: Based on the meta-parameters of the initial universal hidden Markov tree model, the hidden state probability set is determined by a maximum expectation algorithm.

5. The method according to claim 2, characterized in that: The method of establishing an initial general hidden Markov tree model corresponding to the initial ocean SAR image based on the initial multi-scale wavelet coefficients and determining a high-noise area image and a low-noise area image of the initial ocean SAR image comprises: Performing sliding window processing on the initial ocean SAR image based on a window of a preset size, and determining the median of the first scale HH subband of multiple initial multi-scale wavelet coefficients in each window; The median values ​​corresponding to each window are sorted, and a sorting result is output, so as to determine the high-noise region image and the low-noise region image based on the sorting result.

6. The method according to claim 2, characterized in that The method of establishing a high-noise universal hidden Markov tree model corresponding to the high-noise region image and a low-noise universal hidden Markov tree model corresponding to the low-noise region image based on the high-noise multiscale wavelet coefficients and the low-noise multiscale wavelet coefficients respectively comprises: Determine a second probability density function of each of the high-noise multiscale wavelet coefficient and the low-noise multiscale wavelet coefficient; Determining the second node relationship between the high-noise multiscale wavelet coefficients and the low-noise multiscale wavelet coefficients at different scale layers through a parameter set of a hidden Markov tree model; Based on the second probability density function and the second node relationship, the high-noise universal hidden Markov tree model and the low-noise universal hidden Markov tree model are respectively established.

7. The method according to claim 2, characterized in that: The step of determining the wavelet coefficients of the high noise region according to the implicit state probability set of the high noise region image, and determining the wavelet coefficients of the low noise region according to the implicit state probability set of the low noise region image, comprises: The conditional means corresponding to the high noise region image and the low noise region image are determined based on the following formula (3): Among them, Y i Represents the wavelet coefficients of the signal in the image; W i Represents the wavelet coefficients of the parent node at different scales; S i represents the implicit state of node i; m represents the specific value of the implicit state; represents the variance of the wavelet coefficients of node i in the hidden state m; σ 2 represents the variance of the noise; ω i Represents the wavelet coefficient of the parent node of node i; Based on the implicit state probability set of the high noise region image and the implicit state probability set of the low noise region image, the high noise region wavelet coefficient and the low noise region wavelet coefficient are respectively determined based on the following formula (4): in, represents the new wavelet coefficients, y i is the wavelet coefficient representing the signal in the local image (high noise area image or low noise area image).

8. A marine synthetic aperture radar image enhancement device based on regional adaptation, characterized in that: include: A generating module, used for performing discrete wavelet transform processing on the initial ocean SAR image to generate initial multi-scale wavelet coefficients corresponding to the initial ocean SAR image; A processing module, used for establishing an initial general hidden Markov tree model corresponding to the initial ocean SAR image based on the initial multi-scale wavelet coefficients and determining a high-noise area image and a low-noise area image of the initial ocean SAR image, wherein the initial general hidden Markov tree model is used for determining an implicit state probability set of the initial ocean SAR image; The generating module is further used to perform discrete wavelet transform processing on the high-noise region image and the low-noise region image respectively, to generate high-noise multi-scale wavelet coefficients corresponding to the high-noise region image and low-noise multi-scale wavelet coefficients corresponding to the low-noise region image; An establishment module is used to establish a high-noise universal hidden Markov tree model corresponding to the high-noise region image and a low-noise universal hidden Markov tree model corresponding to the low-noise region image based on the high-noise multiscale wavelet coefficients and the low-noise multiscale wavelet coefficients, respectively, wherein the high-noise universal hidden Markov tree model is used to determine the hidden state probability set of the high-noise region image, and the low-noise universal hidden Markov tree model is used to determine the hidden state probability set of the low-noise region image; A determination module, used to determine the wavelet coefficients of the high noise region according to the implicit state probability set of the high noise region image, and to determine the wavelet coefficients of the low noise region according to the implicit state probability set of the low noise region image; An updating module, configured to update the initial multi-scale wavelet coefficients corresponding to the high-noise region image and the low-noise region image in the initial universal hidden Markov tree model based on the high-noise region wavelet coefficients and the low-noise region wavelet coefficients, to obtain an updated target universal hidden Markov tree model; The transformation module is used to perform inverse wavelet transformation on the wavelet coefficients in the target universal hidden Markov tree model to obtain the target ocean SAR image after image enhancement.

9. An electronic device, comprising: A processor, and a memory storing a program, wherein the program comprises instructions, which, when executed by the processor, cause the processor to perform the method according to any one of claims 1 to 7.

10. A non-transitory machine-readable medium storing computer instructions, wherein: The computer instructions are used to cause the computer to execute the method according to any one of claims 1 to 7.

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