De-noising method based on recessive low-rank structure inside and among nonlocal similar image blocks

A non-local similarity and image block technology, which is applied in image enhancement, image data processing, instruments, etc., can solve the problems of only dealing with Gaussian noise and the structure of image blocks is not effectively utilized

Active Publication Date: 2016-09-21
WENZHOU UNIVERSITY
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Problems solved by technology

Although the above-mentioned non-local denoising method based on the similarity of image blocks has achieved good denoising effect, there are also o

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  • De-noising method based on recessive low-rank structure inside and among nonlocal similar image blocks
  • De-noising method based on recessive low-rank structure inside and among nonlocal similar image blocks
  • De-noising method based on recessive low-rank structure inside and among nonlocal similar image blocks

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[0065] The present invention is specifically described below by the embodiment, only for further illustrating the present invention, can not be interpreted as the limitation of protection scope of the present invention, the technical engineer of this field can make some non-essential improvements and improvements to the present invention according to the content of the above-mentioned invention Adjustment.

[0066] Such as figure 1 as shown, figure 1 It is the overall framework of the present invention. The present invention is an image denoising method based on the implicit low-rank structure inside and between non-locally similar image blocks. The specific operating hardware and programming language of the method of the present invention are not limited, and can be written in any language. This other working mode will not be described in detail.

[0067] Embodiments of the present invention adopt a Pentium 4 computer with 3.2G Hz central processing unit and 1G byte intern...

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Abstract

The invention relates to a de-noising method based on recessive low-rank structure inside and among nonlocal similar image blocks. The method mainly comprises steps of: dividing a target image into subblocks with overlapped structure, decomposing each image subblock into a low-rank matrix and a sparse matrix by means of affine transformation; searching similar low-rank matrixes, transforming each low-rank matrix into a vector and enabling the vectors to form a new data matrix, and obtaining the low-rank structure among the image subblocks by using a rapid singular value truncation method; and performing affine transformation on the obtained low-rank data matrix to obtain a result indicative of the de-noised original image subblocks, averaging the overlapped areas of different image subblocks so as to obtain a de-noising result of the overall image. The invention provides the effective and universal image de-noising method. An experimental result shows that the de-noising method is more effective and robust than other classic image de-noising algorithms and has good application prospect.

Description

technical field [0001] The invention relates to the fields of image processing and computer vision, in particular to a denoising method based on the implicit low-rank structure inside and between blocks of non-locally similar image blocks. Background technique [0002] Image denoising is an extremely important and widely researched hotspot in the field of image processing. The purpose is to remove various noise pollution in the image while maintaining the structural features of the image such as edges and textures. The effect of image denoising directly affects the subsequent image processing work, and eliminating image noise is of great significance to the research of image processing. [0003] In general, image denoising algorithms can be divided into two categories: local methods and non-local methods. The local method is to use some kind of kernel to perform convolution operation with the image, and use all the pixels in the neighborhood where the current pixel is locat...

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Application Information

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IPC IPC(8): G06T5/00
CPCG06T5/002
Inventor 张笑钦吴瑞平蒋红星叶修梓
Owner WENZHOU UNIVERSITY
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