A Butterworth filter combined with grey correlation degree weight value ocean remote sensing image denoising method

Abstract

The application discloses a kind of Butterworth filter joint grey correlation degree weight's ocean remote sensing image denoising method, comprising: the ocean remote sensing image containing noise is carried out smoothing filtering, obtains initial noise image;The ocean remote sensing image containing noise and the initial noise image are carried out grey absolute correlation degree analysis, obtain target noise pixel image and target pixel area image;The target noise pixel image and the target pixel area image are carried out grey correlation degree weight denoising, obtain final denoising image.The application effectively distinguishes salt and pepper noise, accurately attacks noise, so as to realize the efficient and meticulous noise suppression of remote sensing image.

Description

Technical Field

[0001] The invention belongs to the technical field of salt and pepper noise removal, and in particular relates to a method for denoising ocean remote sensing images by combining Butterworth filtering with grey correlation weights. Background Art

[0002] Current techniques for removing salt and pepper noise from remote sensing images include Butterworth filtering, mean filtering, median filtering, non-local means filtering (NLM), and the DnCNN convolutional neural network (CNN), an image denoising solution based on a deep learning framework. These existing denoising techniques cannot effectively distinguish between noise and high-frequency signals, resulting in unnecessary smoothing loss of true ground edge information and subtle texture features during the denoising process. When remote sensing images are contaminated by high-intensity noise, existing techniques struggle to accurately identify noise components, making their filter windows susceptible to noise points, thus reducing the accuracy and efficiency of the overall denoising effect. To address the problem of existing denoising techniques lacking effective noise differentiation capabilities when dealing with salt and pepper noise in remote sensing images, this paper proposes a marine remote sensing image denoising method that combines Butterworth filtering with gray correlation weighting. Summary of the Invention

[0003] To solve the above technical problems, the present invention proposes a marine remote sensing image denoising method that combines Butterworth filtering with grey correlation weights to effectively distinguish salt and pepper noise and accurately attack the noise, thereby achieving efficient and detailed noise suppression of remote sensing images.

[0004] To achieve the above object, the present invention provides a method for denoising marine remote sensing images by combining Butterworth filtering with grey correlation weights, comprising:

[0005] Perform smoothing filtering on the ocean remote sensing image containing noise to obtain the initial noise image;

[0006] Performing grey absolute correlation analysis on the ocean remote sensing image containing noise and the initial noise image to obtain a target noise pixel image and a target pixel area image;

[0007] The target noise pixel image and the target pixel area image are subjected to grey correlation weighted denoising to obtain a final denoised image.

[0008] Optionally, obtaining an initial noise image includes:

[0009] The ocean remote sensing image containing noise is input into a Butterworth low-pass filter for processing to obtain the initial noise image.

[0010] Optionally, performing grey absolute correlation analysis on the noisy ocean remote sensing image and the initial noisy image includes:

[0011] Screening the noisy ocean remote sensing image for noise points to obtain a target noise pixel image;

[0012] Noise window pixel gray sequence optimization is performed according to the target noise pixel image and the initial noise image to obtain the target pixel area image.

[0013] Optionally, screening noise points from the noisy ocean remote sensing image includes:

[0014] Selecting the ocean remote sensing image containing noise and the initial noise image, determining a local pixel window of a preset size, and constructing an ordered sequence for comparison and analysis based on the pixel points in the window;

[0015] Mark the grayscale value of the central pixel of the window, construct a reference sequence, and replace the grayscale value of the pixel at the corresponding position in the initial noise image with the grayscale value of the central pixel of the window in the original sequence, while the grayscale values ​​of other pixels remain unchanged;

[0016] Calculate the grey absolute correlation coefficients and further calculate the grey absolute correlation degrees;

[0017] The noise points in the ordered sequence of the comparative analysis are judged according to the gray absolute correlation degree. If the judgment conditions are met, the final denoised image is obtained. If the judgment conditions are not met, the image is combined with the result of the noise window pixel grayscale sequence optimization processing.

[0018] Optionally, performing grey relational weighted denoising on the target noise pixel image and the target pixel region image includes:

[0019] The grayscale sequence of the noise point window optimized by the grey relational analysis is combined with the ordered sequence of the comparative analysis that does not meet the judgment conditions, and the grey relational degree weight correction is performed on the noise pixel values ​​and the contaminated pixel values ​​to obtain the final denoised image.

[0020] Optionally, the judgment condition is that the calculated gray absolute correlation degrees are greater than a preset threshold.

[0021] Optionally, the non-compliance judgment condition is that the calculated gray absolute correlation degrees of each item are not greater than a preset threshold.

[0022] An electronic device comprises: a processor and a memory storing computer program instructions; when the processor executes the computer program instructions, the ocean remote sensing image denoising method combining Butterworth filtering with grey correlation weights is implemented.

[0023] A computer storage medium is characterized in that computer program instructions are stored on the computer storage medium, and when the computer program instructions are executed by a processor, the marine remote sensing image denoising method combining Butterworth filtering with grey correlation weights is implemented.

[0024] Technical Effects of the Invention: The present invention discloses a method for denoising marine remote sensing images using a Butterworth filter combined with a gray correlation weight. The Butterworth filter can effectively reduce noise signals while maintaining sharp image edges. The purpose of applying the Butterworth filter in the present invention is to perform a smoothing filtering operation on noisy images. This preprocessing step aims to construct a smoother reference image, which will serve as the basis for the subsequent comprehensive denoising process. For salt and pepper noise discrimination in remote sensing images, the gray absolute correlation is selected as the benchmark parameter for judging noise pixels, and a specific threshold is set to effectively identify noise pixels in remote sensing images. This step prevents unnecessary smoothing loss of true ground edge information and subtle texture features during the denoising process, thereby improving the processing quality and accuracy of noisy remote sensing images. When the noise density is high, the grayscale value sequence contained within the spatial filtering window is inevitably affected by noise pixels. In particular, the abnormal grayscale values ​​of some severely affected pixels will affect the subsequent denoising effect. This step can identify possible noise pixels or pixel areas that are significantly affected by noise, allowing them to participate in the subsequent filtering and denoising process. Through gray correlation weighted denoising, we can finally obtain the new grayscale values ​​of noise points and pixels significantly affected by noise, thus achieving the denoising effect. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] The accompanying drawings, which constitute part of this application, are intended to provide a further understanding of this application. The exemplary embodiments and descriptions of this application are intended to explain this application and do not constitute an improper limitation on this application. In the accompanying drawings:

[0026] Figure 1 This is a flow chart of a method for denoising ocean remote sensing images using Butterworth filtering combined with grey correlation weights according to an embodiment of the present invention;

[0027] Figure 2 This is a schematic diagram of an image with 2% salt and pepper noise density added according to an embodiment of the present invention;

[0028] Figure 3 This is a schematic diagram of the Butterworth filter denoising result according to an embodiment of the present invention;

[0029] Figure 4 This is a schematic diagram of the mean filtering denoising result according to an embodiment of the present invention;

[0030] Figure 5Schematic diagram of median filtering denoising results in an embodiment of the present invention;

[0031] Figure 6 This is a schematic diagram of the NLM denoising results according to an embodiment of the present invention;

[0032] Figure 7 This is a schematic diagram of the DnCNN denoising results according to an embodiment of the present invention;

[0033] Figure 8 This is a schematic diagram of the results of the method of this solution in an embodiment of the present invention;

[0034] Figure 9 Schematic diagram of the variation trend of peak signal-to-noise ratio (PSNR) with noise density of the filtering method according to an embodiment of the present invention;

[0035] Figure 10 Schematic diagram of the structural similarity index (SSIM) of the filtering method according to an embodiment of the present invention as the noise density changes. DETAILED DESCRIPTION

[0036] It should be noted that, in the absence of conflict, the embodiments and features of the embodiments in this application can be combined with each other. The present application will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0037] It should be noted that the steps shown in the flowcharts of the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions, and that, although a logical order is shown in the flowcharts, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0038] like Figure 1 As shown, this embodiment provides a method for denoising marine remote sensing images using Butterworth filtering combined with grey correlation weights, including:

[0039] Perform smoothing filtering on the ocean remote sensing image containing noise to obtain the initial noise image;

[0040] The grey absolute correlation analysis is performed on the noisy ocean remote sensing image and the initial noise image to obtain the target noise pixel image and the target pixel area image.

[0041] The target noise pixel image and the target pixel area image are denoised using grey correlation weights to obtain the final denoised image.

[0042] Furthermore, obtaining the initial noise image includes:

[0043] The noisy ocean remote sensing image is input into the Butterworth low-pass filter to obtain the initial noise image.

[0044] Specifically, given that noise components are prevalent in the high-frequency range of an image, properly suppressing high-frequency signals can help achieve the goal of image denoising. Using a Butterworth low-pass filter effectively reduces high-frequency noise components in an image, aiming to smooth the noisy image. This preprocessing step aims to create a smoother reference image, which serves as the foundation for the subsequent comprehensive denoising process.

[0045] Furthermore, the grey absolute correlation analysis of the noisy ocean remote sensing image and the initial noise image includes:

[0046] Noise points are screened from noisy ocean remote sensing images to obtain target noise pixel images;

[0047] The noise window pixel gray sequence is optimized according to the target noise pixel image and the initial noise image to obtain the target pixel area image.

[0048] Furthermore, the noise point screening of noisy ocean remote sensing images includes:

[0049] Select the noisy ocean remote sensing image and the initial noise image, determine the local pixel window of preset size, and construct an ordered sequence for comparative analysis based on the pixel points in the window;

[0050] Mark the grayscale value of the central pixel of the window, construct a reference sequence, and replace the grayscale value of the pixel at the corresponding position in the initial noise image with the grayscale value of the central pixel of the window in the original sequence, while the grayscale values ​​of other pixels remain unchanged;

[0051] Calculate the grey absolute correlation coefficients for each item, and further calculate the grey absolute correlation degrees for each item;

[0052] The noise points are judged according to the ordered sequence of comparative analysis based on the gray absolute correlation degree. If the judgment conditions are met, the final denoised image is obtained. If the judgment conditions are not met, the image is combined with the result of the optimization processing of the noise window pixel grayscale sequence. The judgment condition is that the calculated gray absolute correlation degrees are greater than the preset threshold. If the judgment conditions are not met, the calculated gray absolute correlation degrees are not greater than the preset threshold.

[0053] Specifically, the noise image is selected as The size of the local pixel window is 3×3. For all nine pixels in the window, the grayscale values ​​they carry are sorted in ascending order to construct an ordered sequence for subsequent comparative analysis:

[0054]

[0055] Among them, the gray value of the pixel in the center of the window is marked as When constructing the reference sequence, the pixel grayscale value x at the corresponding position in the Butterworth low-pass filtered image is converted to bw (m) Replace into the original sequence The grayscale values ​​of other pixels remain unchanged:

[0056]

[0057] IAGO generates:

[0058]

[0059]

[0060] Wherein, k = 1, 2, ..., 8;

[0061] Calculate the gray absolute correlation coefficient:

[0062]

[0063] Calculate the grey absolute correlation degree:

[0064]

[0065] If the gray absolute correlation degree of the central pixel in a 3×3 window is lower than the preset threshold or the grayscale value of the central pixel in the window is at the maximum or minimum extreme of all the pixels in the window, then the central pixel is likely to be a noise pixel and needs to be weakened. Otherwise, the pixel is considered to be a non-noise point and its original grayscale value should be retained unchanged.

[0066] Furthermore, optimizing the grayscale sequence of noise window pixels according to the target noise pixel image specifically includes: assuming the grayscale value of a 3×3 window of a noise pixel in the image is:

[0067]

[0068] The grayscale value of the Butterworth filtered image at the corresponding window position is:

[0069]

[0070] The comparison sequence used in the noise window pixel grayscale sequence optimization process is the grayscale value sequence of 8 pixels around the noise pixel, and the reference sequence is the grayscale value sequence of the pixel at the same position after filtering in the frequency domain.

[0071] Comparing sequences:

[0072] Reference sequence: xbw (i-1, j-1), x bw (i-1, j), x bw (i-1, j+1), x bw (i, j+1), x bw (i+1, j+1), x bw (i+1, j), x bw (i+1, j-1), x bw (i, j-1)(10);

[0073] Subsequently, the correlation coefficient of each pixel in the noise window and the corresponding gray absolute correlation degree are calculated, and an appropriate threshold is set. Based on this threshold, possible noise pixels or pixel areas that are obviously affected by noise are identified and involved in the subsequent filtering and denoising processing steps.

[0074] Furthermore, performing grey correlation weighted denoising on the target noise pixel image and the target pixel area image includes:

[0075] The grayscale sequence of the noise point window optimized by grey relational analysis is combined with the ordered sequence of comparative analysis that does not meet the judgment conditions, and the grey relational degree weight correction is performed on the noise pixel values ​​and the contaminated pixel values ​​to obtain the final denoised image.

[0076] Specifically, the gray correlation degree is regarded as the weight of the corresponding pixel to replace the uniform weight 1 assigned to each pixel in the traditional mean filter, and the sum of these correlation degree weights replaces the denominator value used for normalization in the traditional filter to perform gray correlation degree weight correction, and finally obtain the new grayscale values ​​of the noise points and pixels significantly affected by noise.

[0077] An electronic device comprises a processor and a memory storing computer program instructions; when the processor executes the computer program instructions, a method for denoising ocean remote sensing images by combining Butterworth filtering with grey correlation weights is implemented.

[0078] A computer storage medium stores computer program instructions, which, when executed by a processor, implement a marine remote sensing image denoising method combining Butterworth filtering with grey correlation weights.

[0079] The present invention discloses a method for denoising marine remote sensing images using a Butterworth filter combined with a gray correlation weight. The Butterworth filter can effectively reduce noise signals while maintaining sharp image edges. The purpose of applying the Butterworth filter in this technique is to perform a smoothing filtering operation on noisy images. This preprocessing step aims to construct a smoother reference image, which serves as the basis for the subsequent comprehensive denoising process. For salt and pepper noise discrimination in remote sensing images, the gray absolute correlation is selected as the benchmark parameter for determining noisy pixels, and a specific threshold is set to effectively identify noisy pixels in remote sensing images. This step prevents unnecessary smoothing loss of true ground edge information and subtle texture features during the denoising process, thereby improving the processing quality and accuracy of noisy remote sensing images. When the noise density is high, the grayscale value sequence contained within the spatial filtering window is inevitably affected by noisy pixels. In particular, the abnormal grayscale values ​​of severely affected pixels can affect the subsequent denoising effect. This step can identify potential noisy pixels or pixel regions significantly affected by noise, allowing them to participate in the subsequent filtering and denoising process. Through gray correlation weighted denoising, we can finally obtain the new grayscale values ​​of noise points and pixels significantly affected by noise, thus achieving the denoising effect.

[0080] A specific application example of the present invention is as follows:

[0081] Taking the simulation of adding 2% salt and pepper noise to the remote sensing image of Qilianyu Island as an example, a Butterworth low-pass filter with a cutoff frequency of 100 and a filter order of 3 was selected. The gray correlation threshold for identifying noise pixels and the gray correlation threshold for optimizing the gray sequence within the window where the noise pixels are located were set to 0.80. The results of this denoising process were compared with those obtained by various filtering techniques. These comparison results are shown in Figure 2. Figures 2 to 8 The form of is shown in Table 1.

[0082] like Figure 5 As shown in the figure, the median filter method does have a good effect on suppressing salt and pepper noise, but it does not have a targeted noise screening function, and its inherent nonlinear properties will reduce the clarity of areas such as the edges of objects and linear features in the image during the denoising operation, thereby indirectly affecting the accuracy of image interpretation. Figure 8 The results of an algorithm combining frequency-domain low-pass filtering with gray correlation weighting are presented. This combined algorithm enables the denoised image to exhibit excellent clarity and relatively complete edge information preservation, significantly enhancing the overall image quality.

[0083] Table 1 and Table 2 are comparative analyses of various filtering technologies used. Figure 9 and Figure 10 The results show the changing trends of the denoising effects of various filtering methods under different noise density conditions. From the overall data, the composite filtering method adopted by the present invention has significant advantages in improving the peak signal-to-noise ratio of the image and increasing the structural similarity index, which is consistent with the previous subjective evaluation based on visual effects. However, when the noise density increases to 50% or even higher, the denoising performance of the combined filtering method will decline to a certain extent due to the dense distribution of noise points, which leads to a significant reduction in the amount of signal that can be effectively extracted by gray relational analysis.

[0084] Table 1

[0085]

[0086] Table 2

[0087]

[0088]

[0089] The above are merely preferred embodiments of the present application, but the scope of protection of the present application is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in this application should be included in the scope of protection of the present application. Therefore, the scope of protection of the present application should be based on the scope of protection of the claims.

Claims

1. A method for denoising marine remote sensing images using Butterworth filtering combined with grey correlation weights, characterized in that: include: Perform smoothing filtering on the ocean remote sensing image containing noise to obtain the initial noise image; Performing grey absolute correlation analysis on the ocean remote sensing image containing noise and the initial noise image to obtain a target noise pixel image and a target pixel area image; Performing grey correlation weighted denoising on the target noise pixel image and the target pixel area image to obtain a final denoised image; Performing grey absolute correlation analysis on the noisy ocean remote sensing image and the initial noise image includes: Screening the noisy ocean remote sensing image for noise points to obtain a target noise pixel image; Optimize the gray sequence of noise window pixels according to the target noise pixel image and the initial noise image to obtain the target pixel area image; Screening noise points from the noisy ocean remote sensing image includes: Selecting the ocean remote sensing image containing noise and the initial noise image, determining a local pixel window of a preset size, and constructing an ordered sequence for comparison and analysis based on the pixel points in the window; Mark the grayscale value of the central pixel of the window, construct a reference sequence, and replace the grayscale value of the pixel at the corresponding position in the initial noise image with the grayscale value of the central pixel of the window in the original sequence, while the grayscale values ​​of other pixels remain unchanged; Calculate the grey absolute correlation coefficients and further calculate the grey absolute correlation degrees; Determine the noise points in the ordered sequence of the comparison analysis according to the gray absolute correlation degree, and obtain the final denoised image if the judgment conditions are met; otherwise, combine the image with the result of the noise window pixel grayscale sequence optimization processing; The gray correlation weighted denoising of the target noise pixel image and the target pixel area image includes: The grayscale sequence of the noise point window optimized by the grey relational analysis is combined with the ordered sequence of the comparative analysis that does not meet the judgment conditions, and the grey relational degree weight correction is performed on the noise pixel values ​​and the contaminated pixel values ​​to obtain the final denoised image.

2. The marine remote sensing image denoising method using Butterworth filtering combined with grey correlation weights as claimed in claim 1, wherein: Obtaining the initial noise image includes: The ocean remote sensing image containing noise is input into a Butterworth low-pass filter for processing to obtain the initial noise image.

3. The marine remote sensing image denoising method using Butterworth filtering combined with grey correlation weights as claimed in claim 1, wherein: The judgment condition is that the calculated gray absolute correlation degree is greater than the preset threshold.

4. The marine remote sensing image denoising method using Butterworth filtering combined with grey correlation weights as claimed in claim 1, wherein: The non-compliance judgment condition is that the calculated gray absolute correlation degrees of each item are not greater than a preset threshold.

5. An electronic device, characterized in that: The device includes: a processor and a memory storing computer program instructions; when the processor executes the computer program instructions, the marine remote sensing image denoising method of Butterworth filtering combined with grey correlation weights as described in any one of claims 1 to 4 is implemented.

6. A computer storage medium, characterized in that The computer storage medium stores computer program instructions, which, when executed by a processor, implement the marine remote sensing image denoising method using Butterworth filtering combined with grey correlation weights as described in any one of claims 1 to 4.

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