Image denoising method and system for sonar carried by underwater remote control robot

Abstract

The invention provides an image denoising method and system for a sonar carried by an underwater remote control robot. The method comprises the following steps: step 1, acquiring an original sonar image of the sonar carried by the underwater remote control robot; 2, preprocessing the original sonar image obtained in the step 1; step 3, carrying out de-noising processing on the sonar image preprocessed in the step 2 by using a sonar image de-noising method based on Alpha-Shapes on the basis of the sonar image de-noising method; and step 4, outputting the sonar image after de-noising processing in the step 3. The method is applied to the field of underwater robot sonar image processing, the denoising processing effect on the sonar image is improved, and the image can be better applied.

Description

Technical Field

[0001] The invention belongs to the field of sonar image processing, and in particular relates to an image denoising method and a system for a sonar carried by an underwater remote-controlled robot. Background Art

[0002] Sonar imaging technology, a core tool for underwater target detection, is widely used in fields such as marine resource exploration and underwater robot navigation. Due to the complex acoustic characteristics of the underwater environment (multipath reflections, biological noise, and turbulent interference), raw sonar images suffer from low resolution and the presence of complex noise (such as speckle noise). Consequently, the quality of raw images is insufficient for direct analysis and application, requiring image denoising and enhancement before use. Currently, mainstream sonar image denoising methods fall into three categories: threshold-based denoising, wavelet-based frequency domain denoising, and deep learning-based image denoising. However, these methods have limitations and drawbacks. For example, threshold-based denoising methods cannot effectively remove noise, and the combination of morphological methods with fixed structuring elements for erosion and dilation can blur the outlines of weakly reflective targets, severely affecting the accuracy of subsequent feature matching. Wavelet-based denoising methods effectively suppress Gaussian white noise but are less effective against non-Gaussian noise. For example, the pulse noise generated by a ship's propeller can cause streaking artifacts in the denoised image. Denoising methods based on deep learning require a large amount of training data, but the acquisition of real underwater sonar datasets is expensive, and the algorithm computational cost is also high. Summary of the Invention

[0003] In view of this, the present invention aims to propose an image denoising method and system for the sonar carried by an underwater remote-controlled robot, which are applied to the field of sonar image processing of underwater robots, improve the denoising effect of sonar images, and enable better application of images.

[0004] To achieve the above object, the technical solution created by the present invention is implemented as follows: A method for denoising an image of a sonar carried by an underwater remote-controlled robot, the method comprising the following steps: Step 1: Obtain the original sonar image of the sonar carried by the underwater remote-controlled robot; Step 2: Preprocess the original sonar image obtained in step 1; Step 3: For the sonar image preprocessed in step 2, use the sonar image denoising method based on Alpha-Shapes to denoise the sonar image; Step 4: Output the sonar image after denoising in step 3.

[0005] Furthermore, the step 2 is specifically as follows: Step 2-1: Crop the original sonar image; Step 2-2: Perform threshold segmentation on the image, and the threshold is adaptively selected according to the grayscale value of the sonar data.

[0006] Furthermore, the step 3 is specifically as follows: Step 3-1: coordinate conversion of sonar image; Step 3-2: Calculate the parameters of the sonar image with the coordinates converted in step 3-1 line by line, and calculate the average Euclidean distance between adjacent points in each scan line; Step 3-3: Based on the average Euclidean distance in step 3-2, outlier detection is performed on the sonar image, and outliers are retained to achieve sonar image denoising.

[0007] Furthermore, the step 3-1 is specifically as follows: Step 3-1-1: Read and identify the sonar image matrix ; Step 3-1-2: Create polar coordinate system mapping, i Line corresponding to sonar detection distance , No. j Column corresponding to azimuth ,

[0008] in, and The size of is determined by the actual detection range of the sonar and the sonar image resolution. Assuming the detection range is , the detection angle is , the image resolution is ; Step 3-1-3: Generate a spatial point set, , .

[0009] Furthermore, the step 3-2 is specifically as follows: for each scan line Calculate the average Euclidean distance between adjacent points in a row, , set the adaptive radius .

[0010] Furthermore, the step 3-3 is specifically as follows: Step 3-3-1: Candidate point set construction: Determine the detection point set , dynamic radius , with the current starting point As the center, search for points that meet the constraint distance: ; in, The initial point is , the subsequent new starting point selects the processed point set The pixel with the largest number; Step 3-3-2: Calculate the center of the circle: Point pair For two points on the scan line, find the radius of the point pair passing through The center of the circle , satisfying the following equation: To optimize the calculation efficiency, the intersection method in surveying and mapping is used to obtain the coordinates of the circle center: ; Step 3-3-3: Outlier determination: Constructing a search circle , detect points inside the circle , let the point set The gray value of the corresponding point is 0, and the outlier point is retained; Step 3-3-4: Loop steps 3-3-1 to 3-3-3 until all pixels in the row are traversed; Step 3-3-5: Parallel calculation of sonar image matrix pixel row scan lines , shorten the calculation time and output the result of the denoised image.

[0011] An image denoising system for a sonar carried by an underwater remote-controlled robot, the system using the above-mentioned image denoising method for a sonar carried by an underwater remote-controlled robot, the system comprising: Original sonar image acquisition module: obtains the original sonar image carried by the underwater remote-controlled robot; Preprocessing module: preprocess the acquired raw sonar images; Sonar image denoising module: For the preprocessed sonar image, the sonar image denoising method based on Alpha-Shapes is used to denoise the sonar image. Sonar image output module: outputs the denoised sonar image.

[0012] Compared with the prior art, the present invention has the following advantages: This invention effectively overcomes the shortcomings of traditional sonar image denoising methods, such as insufficient handling of non-Gaussian noise, poor denoising performance, and high computational cost, by integrating the Alpha-Shapes algorithm with an adaptive parameter mechanism. It achieves sonar image denoising by integrating sonar images with physical spatial information through coordinate mapping and combining it with a row-by-row dynamic radius calculation strategy. The parallel processing architecture optimizes the algorithm, improving computational efficiency and enabling denoising without relying on large-scale training datasets. This provides a solution for sonar image processing in complex underwater environments. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] The accompanying drawings, which constitute part of the present invention, are intended to provide a further understanding of the present invention. The exemplary embodiments of the present invention and their descriptions are intended to explain the present invention and do not constitute an undue limitation of the present invention. In the accompanying drawings: Figure 1 It is the overall flow chart of the present invention.

[0014] Figure 2 This is a flow chart of the sonar image denoising method based on Alpha-Shapes of the present invention.

[0015] Figure 3 The figure shows the comparison between the original noisy image and the denoised image, where (a) is the original noisy image and (b) is the denoised image using the present invention. DETAILED DESCRIPTION

[0016] It should be noted that, in the absence of conflict, the embodiments of the present invention and the features in the embodiments can be combined with each other.

[0017] The present invention will be described in detail below with reference to the accompanying drawings and in combination with embodiments.

[0018] Implementation Method 1 This embodiment provides a method for denoising an image of a sonar carried by an underwater remote-controlled robot, the method comprising the following steps: Step 1: Obtain the original sonar image of the sonar carried by the underwater remote-controlled robot; Step 2: Preprocess the original sonar image obtained in step 1; Step 3: For the sonar image preprocessed in step 2, use the sonar image denoising method based on Alpha-Shapes to denoise the sonar image; Step 4: Output the sonar image after denoising in step 3.

[0019] Furthermore, the step 2 is specifically as follows: Step 2-1: Crop the original sonar image. The cropped part is invalid data of the original image to reduce the calculation time. Step 2-2: Perform threshold segmentation on the image, and the threshold is adaptively selected according to the grayscale value of the sonar data.

[0020] Furthermore, the step 3 is specifically as follows: Step 3-1: coordinate conversion of sonar image; Step 3-2: Calculate the parameters of the sonar image with the coordinates converted in step 3-1 line by line, and calculate the average Euclidean distance between adjacent points in each scan line; Step 3-3: Based on the average Euclidean distance in step 3-2, outlier detection is performed on the sonar image, and outliers are retained to achieve sonar image denoising.

[0021] Furthermore, the step 3-1 is specifically as follows: Step 3-1-1: Read and identify the sonar image matrix (M is the number of matrix pixel rows and N is the number of pixel columns); Step 3-1-2: Create polar coordinate system mapping, i Line corresponding to sonar detection distance , No. j Column corresponding to azimuth ,

[0022] in, and The size of is determined by the actual detection range of the sonar and the sonar image resolution. Assuming the detection range is , the detection angle is , the image resolution is ; Step 3-1-3: Generate a spatial point set, , .

[0023] Furthermore, the step 3-2 is specifically as follows: for each scan line Calculate the average Euclidean distance between adjacent points in a row, , set the adaptive radius .

[0024] Furthermore, the step 3-3 is specifically as follows: Step 3-3-1: Candidate point set construction: Determine the detection point set , dynamic radius , with the current starting point As the center, search for points that meet the constraint distance: ; in, The initial point is , the subsequent new starting point selects the processed point set The pixel with the largest number; Step 3-3-2: Calculate the center of the circle: Point pair For two points on the scan line, find the radius of the point pair passing through The center of the circle , satisfying the following equation: To optimize the calculation efficiency, the intersection method in surveying and mapping is used to obtain the coordinates of the circle center: ; Step 3-3-3: Outlier determination: Constructing a search circle , detect points inside the circle , let the point set The gray value of the corresponding point is 0, and the outlier point is retained; Step 3-3-4: Loop steps 3-3-1 to 3-3-3 until all pixels in the row are traversed; Step 3-3-5: Parallel calculation of sonar image matrix pixel row scan lines , shorten the calculation time and output the result of the denoised image.

[0025] like Figure 3 As shown, (b) is the sonar image after denoising using the present invention, and the denoising effect is extremely significant compared with the original noisy image in (a).

[0026] Implementation Method 2 This embodiment provides an image denoising system for a sonar carried by an underwater remote-controlled robot. The system uses the image denoising method for a sonar carried by an underwater remote-controlled robot as described in Embodiment 1. The system includes: Original sonar image acquisition module: obtains the original sonar image carried by the underwater remote-controlled robot; Preprocessing module: preprocess the acquired raw sonar images; Sonar image denoising module: For the preprocessed sonar image, the sonar image denoising method based on Alpha-Shapes is used to denoise the sonar image. Sonar image output module: outputs the denoised sonar image.

[0027] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A method for denoising images of a sonar carried by an underwater remote-controlled robot, characterized by: The method comprises the following steps: Step 1: Obtain the original sonar image of the sonar carried by the underwater remote-controlled robot; Step 2: Preprocess the original sonar image obtained in step 1; Step 3: For the sonar image preprocessed in step 2, use the sonar image denoising method based on Alpha-Shapes to denoise the sonar image; Step 4: Output the sonar image after denoising in step 3.

2. The method according to claim 1, wherein: The step 2 is specifically as follows: Step 2-1: Crop the original sonar image; Step 2-2: Perform threshold segmentation on the image, and the threshold is adaptively selected according to the grayscale value of the sonar data.

3. The method according to claim 1, wherein: The step 3 is specifically as follows: Step 3-1: coordinate conversion of sonar image; Step 3-2: Calculate the parameters of the sonar image whose coordinates have been converted in step 3-1 line by line, and calculate the average Euclidean distance between adjacent points in each scan line; Step 3-3: Based on the average Euclidean distance in step 3-2, outlier detection is performed on the sonar image, and outliers are retained to achieve sonar image denoising.

4. The method according to claim 3, wherein: The step 3-1 is specifically as follows: Step 3-1-1: Read and identify the sonar image matrix ; Step 3-1-2: Create polar coordinate system mapping, i Line corresponding to sonar detection distance , No. j Column corresponding to azimuth , in, and The size of is determined by the actual detection range of the sonar and the sonar image resolution. Assuming the detection range is , the detection angle is , the image resolution is ; Step 3-1-3: Generate a spatial point set, , .

5. The method according to claim 4, characterized in that: The step 3-2 is specifically as follows: for each row of scan lines Calculate the average Euclidean distance between adjacent points in a row, , set the adaptive radius .

6. The method according to claim 5, characterized in that: The step 3-3 is specifically as follows: Step 3-3-1: Candidate point set construction: Determine the detection point set , dynamic radius , with the current starting point As the center, search for points that meet the constraint distance: ; in, The initial point is , the subsequent new starting point selects the processed point set The pixel with the largest number; Step 3-3-2: Calculate the center of the circle: Point pair For two points on the scan line, find the radius of the point pair passing through The center of the circle , satisfying the following equation: To optimize the calculation efficiency, the intersection method in surveying and mapping is used to obtain the coordinates of the circle center: ; Step 3-3-3: Outlier determination: Constructing a search circle , detect points inside the circle , let the point set The gray value of the corresponding point is 0, and the outlier point is retained; Step 3-3-4: Loop steps 3-3-1 to 3-3-3 until all pixels in the row are traversed; Step 3-3-5: Parallel calculation of sonar image matrix pixel row scan lines , shorten the calculation time and output the result of the denoised image.

7. An image denoising system for a sonar carried by an underwater remote-controlled robot, characterized by: The system uses the image denoising method of the sonar carried by the underwater remote-controlled robot as described in any one of claims 1 to 6, and the system includes: Original sonar image acquisition module: obtains the original sonar image carried by the underwater remote-controlled robot; Preprocessing module: preprocess the acquired raw sonar images; Sonar image denoising module: For the preprocessed sonar image, the sonar image denoising method based on Alpha-Shapes is used to denoise the sonar image. Sonar image output module: outputs the denoised sonar image.

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