Non-Blind Image Deblurring via Shear Total Variation Norm

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外文摘要：In this paper,we propose a novel shear gradient operator by combining the shear and gradient operators.The shear gradient op-erator performs well to capture diverse directional information in the image gradient domain.Based on the shear gradient operator,we ex-tend the total variation(TV)norm to the shear total variation(STV)norm by adding two shear gradient terms.Subsequently,we introduce a shear total variation deblurring model.Experimental results are provided to validate the ability of the STV norm to capture the detailed in-formation.Leveraging the Block Circulant with Circulant Blocks(BCCB)structure of the shear gradient matrices,the alternating direction method of multipliers(ADMM)algorithm can be used to solve the proposed model efficiently.Numerous experiments are presented to verify the performance of our algorithm for non-blind image deblurring.

外文关键词：

image deblurringshear total variation(STV)normalternating direction method of multipliers(ADMM)Block Circulant with Circulant Blocks(BCCB)matrix

作者：

LI Weiyu、ZHANG Tao、GAO Qiuli

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作者单位：

School of Microelectronics and Data Science,Anhui University of Technology,Maanshan 243032,Anhui,China

基金：

Open Fund of Key Laboratory of Anhui Higher Education InstitutesNational Natural Science Foundation of ChinaOutstanding Young Talents Support Program of Anhui Province

项目编号：

CS2021-0761701004gxyq2021178

出版年：

2024

DOI：

10.1051/wujns/2024293219

武汉大学自然科学学报(英文版)

武汉大学

武汉大学自然科学学报(英文版)

CSTPCD

影响因子：0.066

ISSN：1007-1202

年,卷(期)：2024.29(3)