Image denoising via 2D dictionary learning and adaptive hard thresholding

Authors:
Xuande Zhang;Xiangchu Feng;Weiwei Wang;Guojun Liu
Affiliations:
Department of Applied Mathematics, Xidian University, Xi'an, China and School of Mathematics and Computer Science, Ningxia University, Yinchuan, China;Department of Applied Mathematics, Xidian University, Xi'an, China;Department of Applied Mathematics, Xidian University, Xi'an, China;School of Mathematics and Computer Science, Ningxia University, Yinchuan, China
Venue:
Pattern Recognition Letters
Year:
2013

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Abstract

There is extensive interest in taking advantage of self-similarity inherent in images to learn adaptive dictionary for effective image representation and denoising in recent years. In this letter, we present a complementary view. When a group of similar patches are arranged into the so called similarity data matrix (SDM), there exist linear correlations among both columns and rows of the SDM. With this observation, we propose an image denoising algorithm based on 2D dictionary learning and adaptive hard thresholding (2DDL-AHT). In this algorithm, both row-correlation and column-correlation of the SDM are explored by 2D dictionary learning, and a group of similar patches are estimated by using adaptive hard thresholding. The experiments indicate that the proposed algorithm performs on par or slightly better than the state-of-the-art denoising methods.