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Multi-directional weighted TV and non local self-similarity regularization image deblurring method

A self-similarity and deblurring technology, applied in image enhancement, image data processing, instruments, etc., can solve problems such as false edges and wrinkles that cannot be solved well

Inactive Publication Date: 2016-03-23
TIANJIN UNIV
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Problems solved by technology

This type of method can restore image details better, but still cannot solve the problem of false edges and wrinkles

Method used

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  • Multi-directional weighted TV and non local self-similarity regularization image deblurring method
  • Multi-directional weighted TV and non local self-similarity regularization image deblurring method
  • Multi-directional weighted TV and non local self-similarity regularization image deblurring method

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Embodiment Construction

[0083] The technical solution of the present invention will be further described in detail below in conjunction with the accompanying drawings and specific embodiments. The specific embodiments described are only for explaining the present invention and are not intended to limit the present invention.

[0084] The present invention is a multi-directional weighted TV and non-local self-similarity regularized image deblurring method (MDWTV-NLR method). Its essence is to use the unique edge detection-based multi-directional weighted TV and non-local After solving the three sub-problems of blurred image f, clear image x, and auxiliary variable u, the self-similarity regularized image deblurring model will iteratively update the parameters λ and ν, and execute iteratively until the optimal solution is obtained, that is, a blurred image The clear image x is obtained after deblurring. Specific steps are as follows:

[0085] Step 1: Establish an image deblurring model based on edge detect...

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Abstract

The invention discloses a multi-directional weighted TV and non local self-similarity regularization image deblurring method. The method mainly comprises: solving three sub-problems of a blurred image f, a brilliant image x and an auxiliary variable u by utilizing a specific multi-directional weighted TV and non local self-similarity regularization image deblurring model obtained by adding edge detection into a weighted TV deblurring model and integrating a non local self-similarity regularization item; performing iterative updating on parameters lambda and v; and performing periodic execution until an optimal solution is obtained, namely, deblurring the blurred image to obtain the brilliant image x. Experimental results show that a very good deblurring effect can be achieved after the blurred image is deblurred with the method; compared with the prior art, due to the adoption of the edge detection, the edges can be better processed, and not only be false edges eliminated but also textures and details of an image can be better reserved; and meanwhile, the SNR of the image is increased and a better visual effect is achieved.

Description

Technical field [0001] The invention belongs to the field of computer image processing and can be used in image / video deblurring and other related fields. Background technique [0002] Image deblurring has always been a research hotspot in the field of computer vision and image processing. It has attracted much attention because of its cutting-edge and wide application characteristics. [0003] Among the many deblurring methods, TV regularization is widely used in image denoising and image deblurring due to its better edge retention ability. [1,2] , But its details and texture restoration capabilities are limited. Therefore, there have been many TV regularization improvements, such as weighted TV (WTV) regularization, which improves the detail recovery ability of the TV model by applying different weights to the smooth area and the edge of the image. [3] , But false edges and wrinkles are prone to appear in areas with more texture and detail. [0004] On the other hand, the traditio...

Claims

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Application Information

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IPC IPC(8): G06T5/00
CPCG06T5/73
Inventor 杨爱萍魏宝强田玉针何宇清张越
Owner TIANJIN UNIV
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