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Image denoising method based on sparse regularization

An image and sparse technology, applied in image enhancement, image data processing, instruments, etc., can solve problems such as large time complexity

Inactive Publication Date: 2016-01-27
EAST CHINA JIAOTONG UNIVERSITY
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Problems solved by technology

However, due to the use of the PCA dictionary, the time complexity is large

Method used

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  • Image denoising method based on sparse regularization
  • Image denoising method based on sparse regularization
  • Image denoising method based on sparse regularization

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Embodiment Construction

[0033] Refer to attached figure 1 , the implementation steps of the present invention are as follows:

[0034] Step 1: Input a noise image y with a size of 256×256;

[0035] Step 2: divide the image into blocks, and divide these image blocks into K clusters according to the degree of similarity;

[0036] Step 3: For each cluster, learn the sparse K-SVD dictionary on it. For each given image block, firstly judge which cluster it belongs to, and then use the sparse K-SVD dictionary corresponding to the cluster as D.

[0037] Step 4: Build a sparse regularization denoising model:

[0038] x ^ = arg min X , F { 1 2 σ 2 | | y - x | | 2 + ...

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Abstract

The invention relates to an image denoising method based on sparse regularization. The image denoising method based on sparse regularization fully utilizes the gradient information of an image and non local self-similarity to construct a sparse regularization denoising model, and utilizes an iteration histogram standardization algorithm to solve the model. The solving process includes: dividing the image into image blocks, and then clustering the image blocks according to the level of structural similarity, and at last using a sparse K-SVD dictionary to train each given image block. Therefore, the structural property of the dictionary is improved and also the over-complete dictionary obtained through training can preferably perform sparse representation of the image blocks. The image denoising method based on sparse regularization has the advantages of effectively denoising the image, being high in the reservation capability for the image texture structure, obtaining a better image visual effect, further reducing the computation complexity, and improving the operation speed.

Description

technical field [0001] The invention belongs to the field of computer image processing and relates to an image denoising method based on sparse regularization. Background technique [0002] In the process of acquisition, conversion or transmission, due to the influence of external factors, equipment, etc., the image will inevitably introduce various noises. The existence of noise will degrade the image quality and affect the subsequent image processing. Therefore, image denoising has become the most basic and critical link in the image processing process. It aims to retain the important information of the image after removing the noise, and obtain an image as close as possible to the original image. [0003] In recent years, based on the characteristics of sparse adaptability, irrelevance, and atomization, using image sparse representation to achieve denoising is currently a popular denoising method. It first uses an over-complete dictionary to adaptively represent images ...

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Application Information

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IPC IPC(8): G06T5/00
Inventor 罗晖汪玉珍王培东王玮张桓余文苑
Owner EAST CHINA JIAOTONG UNIVERSITY
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