Title: Infrared image denoising using a smoothed L0 sparse regression

Authors: Shengqian Wang; Jianping Xiao; Chengzhi Deng

Addresses: School of Information Engineering, Nanchang Institute of Technology, Nanchang, Jiangxi Province, 330099, China ' School of Communication and Electronic, Jiangxi Science and Technology Normal University, Nanchang, Jiangxi Province, 330013, China ' School of Information Engineering, Nanchang Institute of Technology, Nanchang, Jiangxi Province, 330099, China

Abstract: Sparse representation based on image denoising has acquired considerable interest. In the most previous work on the sparse representation, L1 and L0 norm are always used as the sparsity regularisation. Despite the success of L1 or L0 norm, the limitation of this approach on its computational complexity or sparsity affects the efficiency or accuracy. In this paper, a smoothed L0 approach based on infrared image denoising is proposed. Firstly, an improved smoothed L0-based K-SVD (SL0-KSVD) method for dictionary learning is presented. And then the infrared images are sparsely represented by the smoothed L0 method and the coefficients are denoised using the constantly updated K-SVD dictionary. Finally, some experiments are taken on comparing the peak signal to noise ratio (PSNR) performance of our proposed method with its counterparts on different images. The experimental results on both the visualisation and real data of the infrared images demonstrate the superiorities of our proposed method.

Keywords: sparse representation; infrared images; image denoising; dictionary learning; K-SVD; peak SNR; signal to noise ratio; PSNR.

DOI: 10.1504/IJCSM.2015.069748

International Journal of Computing Science and Mathematics, 2015 Vol.6 No.3, pp.241 - 255

Available online: 28 May 2015 *

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