Edge-based multi-direction weighting TV and self-similarity constraint image defuzzification method

A self-similarity and deblurring technology, applied in image enhancement, image data processing, instruments, etc., can solve problems such as insufficient image prior information expression ability, limited ability to restore details and texture, and error of spatial domain weight matrix
CN106485671AInactive Publication Date: 2017-03-08TIANJIN UNIV
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
TIANJIN UNIV
Publication Date
2017-03-08
Estimated Expiration
Not applicable · inactive patent

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Abstract

The invention discloses an edge-based multi-direction weighting TV and self-similarity constraint image defuzzification method. A conventional TV model is improved based on edge detection, and transform domain self-similarity of an image is combined. Firstly, pixels in a center pixel neighborhood is divided into same-side pixel pairs and different-side pixel pairs by edge detection, different weights are adopted for different types of the pixels to obtain a multi-direction weighting TV algorithm, and the detailed information of the image is reserved to the greatest extent in defuzzification; secondly, a transform domain self-similarity regularization term is fused into the TV model, so that the limitation that the conventional non-local regularization depends on a non-local weight matrix is overcome; therefore, the texture and structural self-similarity of the image can be described more accurately; and the defuzzification visual effect of the image is further improved. By adoption of the defuzzification method, the visual effect is improved by defuzzification while the edge information and important details of the image are well reserved; and in addition, the objective indicators, such as a peak signal to noise ratio and the like, of the image are improved.
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Description

technical field

[0001] The invention belongs to the field of computer image processing, and is mostly used in related fields such as image or video deblurring. Background technique

[0002] Image deblurring has always been a hot spot in the field of computer vision and image processing, and has attracted much attention because of its cutting-edge and wide application. Among many deblurring methods, total variation (TV) regularization is widely used in image denoising, image deblurring, etc. [1,2] , but its detail and texture recovery capabilities are limited.

[0003] On the other hand, the traditional TV model only considers the local features of the image, and does not utilize the structurally similar global image block information in the image, that is, the non-local self-similarity of the image [1] , the ability to express the prior information of the image is insufficient. Existing methods incorporate spatial non-local self-similarity into deblurring models [5] , bu...

Claims

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