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Adaptive Weighted TGV Image Deblurring Method Based on Primal Dual Algorithm

An adaptive weighting and primitive dual technology, applied in image enhancement, image data processing, calculation, etc., can solve the problems of obvious step effect in flat area and noise amplification, and achieve the effect of avoiding step effect, fast convergence and maintaining image edge

Inactive Publication Date: 2020-11-27
TIANJIN UNIV
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Problems solved by technology

[0016] Aiming at the problems that the traditional TV model only considers the first-order gradient feature of the image, resulting in noise amplification and obvious step effect in the flat area, the present invention proposes an adaptive weighted Generalized Variation (Total Generalized Variation: TGV) deblurring method

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  • Adaptive Weighted TGV Image Deblurring Method Based on Primal Dual Algorithm
  • Adaptive Weighted TGV Image Deblurring Method Based on Primal Dual Algorithm
  • Adaptive Weighted TGV Image Deblurring Method Based on Primal Dual Algorithm

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[0093] The technical solution of the present invention will be further described in detail below in conjunction with the accompanying drawings and specific embodiments, and the described specific embodiments are only for explaining and illustrating the present invention, and are not intended to limit the present invention.

[0094] A kind of self-adaptive weighted TGV image deblurring method based on primitive dual algorithm that the present invention proposes comprises the following steps:

[0095] Step 1. Establish an adaptive weighted TGV image deblurring model

[0096] (1) TV regularization deblurring model

[0097] Assume For a clear N×N image, is a fuzzy operator, is the observed blurred image. The TV regularized image restoration model can be expressed as:

[0098]

[0099] In formula (1), Denote the horizontal and vertical difference operators respectively; the first item is the TV regularization item, and the second item is the data fidelity item; β>0 is ...

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Abstract

The invention discloses a primal-dual-algorithm-based adaptive weighted TGV image de-blurring method. The method comprises the following steps: establishing an adaptive weighted TGV image de-blurringmodel; solving the adaptive weighted TGV image de-blurring model based on a primal-dual algorithm; solving an iterative formula according to the model obtained at the step two; processing an observedimage; and then obtaining a clear image finally. The TGV is capable of approximating a random-order polynomial function, so that de blurring is realized and a staircase effect is avoided; a weight value is adjusted adaptively based on a local structure of an image and thus the image edge is kept and noises are suppressed. Meanwhile, on the basis of the primal-dual algorithm concept, a primal-dual-based adaptive weighted TGV de-blurring iterative algorithm is deduced. The experiment result shows that a high-quality restored image is obtained by the provided de-blurring model; and the provided solution algorithm has advantages of fast convergence and high robustness.

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 research hotspot 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. because of its better edge preservation ability. [1][2] , but the traditional TV model only considers the first-order gradient features of the image, and the deblurred image may have problems such as noise amplification and obvious step effect in the flat area. On the other hand, many algorithms currently used to solve the total variation (TV) regularization correlation model have slow convergence speed and complicated iterative process. [3][4][5] It is difficult to meet the rea...

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06T5/00
Inventor 杨爱萍王金斌张越何宇清
Owner TIANJIN UNIV
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