Image denoising method based on external non-local self-similarity and improved sparse representation

A sparse representation, self-similar technology, applied in the field of image processing, can solve the problems of loss of detail information, low peak signal-to-noise ratio of denoised images, etc., to achieve the effect of improving adaptability, improving peak signal-to-noise ratio, and efficiently removing noise

Active Publication Date: 2021-05-14
SICHUAN CHANGHONG ELECTRIC CO LTD
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Problems solved by technology

[0008] The purpose of the present invention is to address the above-mentioned problems in the prior art, to propose an image denoising method based on external non-local self-similarity and improved sparse representation, by optimizing th

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  • Image denoising method based on external non-local self-similarity and improved sparse representation
  • Image denoising method based on external non-local self-similarity and improved sparse representation
  • Image denoising method based on external non-local self-similarity and improved sparse representation

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Embodiment 1

[0045] Such as figure 1 As shown, it is an image denoising method based on external non-local self-similarity and improved sparse representation. The algorithm needs to be iteratively processed to complete the denoising process. In order to improve the efficiency of denoising, only when the number of iterations is odd Clustering is performed on groups of noisy image blocks. Furthermore, the smooth block ratio of the noisy image only needs to be computed at the first iteration.

[0046] The steps of an image denoising method based on external non-local self-similarity and improved sparse representation are as follows:

[0047] (1) Divide the external clean image dataset into block groups

[0048] Take the Kodak PhotoCD dataset (http: / / r0k.us / graphics / kodak / ) as an external clean image dataset. The dataset contains 24 high-quality natural images, and the size of each image is 500×500. If the side length p of the image block is set to 6, the size of the image block is 6×6. In...

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Abstract

The invention discloses an image denoising method based on external non-local self-similarity and improved sparse representation. The method comprises the following steps: (1) dividing an external clean image data set into block groups; (2) dividing the block group of the external clean image into an external smooth block group and an external texture block group; (3) learning external smooth block group priori; (4) learning external texture block group priori through a Gaussian mixture model; (5) dividing the noise image into block groups; (6) using external non-local self-similarity prior to guide the clustering of the noise image block group, and calculating the smooth block group ratio of the noise image; and (7) optimizing regularization parameters of the sparse representation model by using a smooth block group ratio, and respectively recovering the image blocks in each subspace according to the improved sparse representation model. By optimizing regularization parameters of the sparse representation model, the self-adaptability of the sparse representation model is improved, and the technical problems that in an existing image denoising method, the peak signal-to-noise ratio of a denoised image is low, and detail information is lost are solved.

Description

technical field [0001] The invention relates to the technical field of image processing, in particular to an image denoising method based on external non-local self-similarity and improved sparse representation. Background technique [0002] After decades of development, digital image processing technology has become more and more mature, and has played a great role in the fields of climate monitoring, medical diagnosis, face recognition and defect detection. Digital images will inevitably be disturbed by noise in the process of generation, transmission and conversion, and operations such as compression and feature extraction of images containing noise will cause degradation of image quality. Therefore, the problem of estimating the original clean image from the noisy image has become one of the important research contents. [0003] Image denoising algorithms are mainly divided into spatial domain methods and transform domain methods. The spatial domain methods use the corr...

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Application Information

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IPC IPC(8): G06T5/00G06T7/44G06T7/90
CPCG06T5/002G06T7/44G06T7/90
Inventor 白同磊牛小明赵磊冷成财
Owner SICHUAN CHANGHONG ELECTRIC CO LTD
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