Denoising for Multiple Image Copies through Joint Sparse Representation

Authors:
Nannan Yu;Tianshuang Qiu;Fuquan Ren
Affiliations:
Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, Dalian, P.R. China 116024 and Electronic and Information Engineering College, Northeast Petroleum Uni ...;Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, Dalian, P.R. China 116024;Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, Dalian, P.R. China 116024
Venue:
Journal of Mathematical Imaging and Vision
Year:
2013

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Abstract

This paper addresses the recovery of original images from multiple copies corrupted with the noises, which can be represented sparsely in some dictionary. Sparse representation has been proven to have strong ability to denoise. However, it performs suboptimally when the noise is sparse in some dictionary. A novel joint sparse representation (JSR)-based image denoising method is proposed. The images can be recovered well from multiple noisy copies. All copies share a common component--the image, while each individual measurement contains an innovation component--the noise. Our method can separate the common and innovation components, and reconstruct the images with the sparse coefficients and the dictionaries. Experiment results show that the performance of the proposed method is better than that of other methods in terms of the metric and the visual quality.