Computer Science

CLC number: TP751

Non-Blind Image Deblurring via Shear Total Variation Norm

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

^† Corresponding author. E-mail: zt9877@163.com

Received: 2 November 2023

Abstract

In this paper, we propose a novel shear gradient operator by combining the shear and gradient operators. The shear gradient operator performs well to capture diverse directional information in the image gradient domain. Based on the shear gradient operator, we extend 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 information. 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.