Research on image deblurring based on second order total variation and Lp pseudo-norm

YANG Jing-jing; CHEN Ying-pin

Chinese

您当前的位置：

首页 >

文章列表页 >

Research on image deblurring based on second order total variation and Lp pseudo-norm

更新时间：2026-09-04

- Research on image deblurring based on second order total variation and Lp pseudo-norm
- Vol. 37, Issue 9, Pages: 18-23(2020)
- 作者机构：
  
  1. 福州大学物理与信息工程学院,福建,福州,350000
  2. 闽南师范大学机构物理与信息工程学院,福建,漳州,363000
- 作者简介：
- 基金信息：
- DOI：
  CLC：
- Published：2020
- 稿件说明：
移动端阅览
杨晶晶, 陈颖频. 基于二阶全变分与Lp伪范数的图像解模糊研究[J]. 微电子学与计算机, 2020, 37(9): 18-23.

YANG Jing-jing, CHEN Ying-pin. Research on image deblurring based on second order total variation and Lp pseudo-norm[J]. 2020, 37(9): 18-23.
杨晶晶, 陈颖频. 基于二阶全变分与Lp伪范数的图像解模糊研究[J]. 微电子学与计算机, 2020, 37(9): 18-23. DOI：

YANG Jing-jing, CHEN Ying-pin. Research on image deblurring based on second order total variation and Lp pseudo-norm[J]. 2020, 37(9): 18-23. DOI：

摘要

各向异性全变分模型广泛应用于图像解模糊

然而

传统的各向异性全变分模型存在以下两个缺点:首先

传统各向异性全变分模型假设图像为分片常数且只考虑了图像一阶梯度的稀疏约束导致恢复的图像存在严重的阶梯伪影效应; 其次传统全变分模型采用L1范数来刻画图像梯度的稀疏性，然而L1范数刻画图像的稀疏性的能力有限，为改进这两个缺点，本文提出一种结合了二阶梯度稀疏约束和Lp伪范数新的去模糊方法，引入二阶全变分是为了适应图像的先验项从而去除阶梯效应，引入Lp伪范数是为了更准确刻画图像的稀疏性，为求解模型，采用交替乘子迭代法将模型分解为若干个去耦合的子问题.通过实验展现本文提出方法的效果.结果显示相比于本文提到的其他四种模型，本文提出的模型具有更优的效果。

Abstract

Anisotropic total variation (ATV) regularization is widely used in image deblurring applications. However

the traditional ATV model suffers from at least two limitations. Firstly

the traditional ATV model assumes the image to be piecewise constant and only considers the sparsity of the first-order image gradients

resulting in staircase artifacts. Secondly

the traditional ATV model employs the L1-norm to depict the sparsity of image gradients. Nonetheless

the L1-norm has a limited capability of depicting the sparsity of sparse variables. To overcome the two challenges

a new deblurring model is presented via the second-order gradient sparse constraint and Lp-pseudo-norm shrinkage. The second-order total variation is incorporated for fitting the image prior term and relieving the staircase artifacts. The Lp-pseudo-norm shrinkage is employed to depict sparse variables precisely. The alternating direction method of multipliers is employed to decompose the presented model into several decoupled subproblems. Finally

experiments are carried out to show the performance of the proposed method. The results show that the proposed method outperforms the other deblurring models mentioned in this article.

关键词

Keywords

references

CHEN Y P, PENG Z M, ALI G, et al. Seismic signal time-Frequency representation by Lp-quasinorm constraint.[J]. Digital Signal Processing, 2019(87): 43-59.

LI S, HE Y M, CHEN Y P, et al. Fast multi-trace impedance inversion using anisotropic total p-variation regularization in the frequency domain[J]. Journal of Geophysics and Engineering, 2018, 15(5):2171-2182. DOI: 2171

WANG X, DING Y. A Filtering Algorithm for Removing Cauchy Noise in Images[J]. Acta Mathematica Scientia, 2018, 38A(4): 823-832. DOI: 823

RUDIN LI, OHER S. Fatemi E.Nonlinear total variation based noise removal algorithms[J]. Physica D:Nonlinear Phenomena, 1992, 60(1/4):259-268. DOI: 259

LIU X G, CHEN Y P, PENG Z M, et al. Infrared image super-resolution reconstruction based on quaternion fractional order total variation with Lp quasinorm[J]. Applied Sciences, 2018(8):1864. DOI: 1864

LIU F, CHEN Y P, WANG L Z, et al. An efficient image reconstruction framework using total variation regularization with Lp-quasinorm and group gradient sparsity[J]. Information, 2019(10): 115-117. DOI: 115

GONZáLEZ G, KOLEHMAINEN V, SEPPäNEN A. Isotropic and anisotropic total variation regularization in electrical impedance tomography[J]. Computers % Mathematics with Applications, 2017, 74(3): 564-576. DOI: 564

BOYD S, PARIKH N, CHU E, et al. Distributed optimization and statistical learning via the alternating direction method of multipliers[J]. Foundations and Trends® in Machine learning, 2011, 3(1): 120-122. DOI: 120

Views

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

Steganography model guided by attention mechanism and improved dense convolution

Implementation and verification of a dual-chain power gating strategy for low power design

A high-efficiency charge pump for ultra-low voltage boost systems

Lightweight countermeasures against Spectre attacks on the branch prediction unit

Aerial vehicle detection algorithm based on multi-scale fusion and small-object enhancement

Related Author

No data

Related Institution

No data

AI问答

Postal code：100079
Tel：（010）53879206 Email：tmw@bjxintong.com.cn
Technical support is provided by Beijing Founder electronics co., LTD 京ICP备09082226号-64 京公网安备11010602201714号
It is recommended to read the content of this site in Chrome&IE9+. Please switch to extreme mode in browser 360.
Cookies We use cookies to help provide and enhance our service and tailor content. By continuing, you agree to the use of cookies.

⁰