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Image Denoising Method Based on Superpixel Clustering and Sparse Representation

A superpixel clustering and sparse representation technology, applied in the field of digital image processing, can solve the problems of low peak signal-to-noise ratio and loss of detail information in denoised images

Active Publication Date: 2019-11-01
XIDIAN UNIV
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Problems solved by technology

[0006] The purpose of the present invention is to overcome the defects in the above-mentioned prior art, and propose an image denoising method based on superpixel clustering and sparse representation to solve the peak signal noise of the denoising image existing in the existing image denoising method Technical issues with low ratio and loss of details

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[0029] Below in conjunction with accompanying drawing and specific embodiment, the present invention is described in further detail:

[0030] refer to figure 1 , an image denoising method based on superpixel clustering and sparse representation, comprising the following steps:

[0031] Step 1, input an image I containing Gaussian white noise with standard deviation δ n .

[0032] In this embodiment, a grayscale image with a resolution of 512×512 is used.

[0033] Step 2, first set the image I n The number of superpixels is R, and for image I n Perform superpixel segmentation to obtain superpixel set {SP i |i=1,2,...,R}, secondly define an empty similarity matrix S, and calculate the superpixel set {SP i Every two superpixels in |i=1,2,...,R} The similarity between them, and store the calculation results in the similarity matrix S, where i is the superpixel set {SP i |i=1,2,...,R} the serial number of the superpixel, SP i is the set of superpixels {SP i |i=1,2,...,R}...

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Abstract

The present invention proposes an image denoising method based on superpixel clustering and sparse representation, which is used to solve the technical problems of low peak signal-to-noise ratio and loss of detail information of denoising images existing in existing image denoising methods, and the implementation steps 1. Input an image to be denoised; 2. Perform superpixel segmentation and superpixel clustering on the image to obtain multiple clusters of similar superpixels; 3. Perform image block extraction and dictionary training for each cluster of similar superpixels; 4 . Calculate the sparse coefficient of each image block under the corresponding dictionary; 5. Find similar image blocks of each image block, and calculate the weighted sum of sparse coefficients of similar image blocks; 6. Utilize the weighted sum of sparse coefficients of similar image blocks, Constrain the sparse decomposition process of each image block to obtain new sparse coefficients; 7. Determine whether the current iteration number is greater than the maximum iteration number Λ, if so, perform step 8, otherwise, increase the number of iterations by 1, and perform step 5; 8. Repeat Construct the image to be denoised to obtain the denoised image.

Description

technical field [0001] The invention belongs to the technical field of digital image processing, and relates to an image denoising method, in particular to an image denoising method based on superpixel clustering and sparse representation, which can be applied to image classification, target recognition, edge detection, etc. In the case of denoising preprocessing. Background technique [0002] Due to the limitations of imaging equipment and imaging environment, digital images are inevitably polluted by noise in the process of acquisition, conversion or transmission. The presence of noise degrades image quality and affects subsequent image processing. In order to obtain a high-quality image, it is necessary to denoise the image. Therefore, image denoising occupies an important position in the field of image processing. [0003] With the continuous development of image denoising technology at home and abroad, researchers have proposed many image denoising methods. At prese...

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06K9/40G06T5/00G06K9/46G06K9/62
CPCG06T5/002G06T2207/20021G06V10/30G06V10/40G06V10/513G06F18/22G06F18/2193
Inventor 王海肖雪赵伟刘岩秦红波
Owner XIDIAN UNIV
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