Adaptive Denoising Using a Modified Sparse Coding Shrinkage Method

Authors:
Li Shang;Feng-Wen Cao
Affiliations:
Department of Electronic Information Engineering, Suzhou Vocational University, Jiangsu, China 215104;Department of Electronic Information Engineering, Suzhou Vocational University, Jiangsu, China 215104
Venue:
Neural Processing Letters
Year:
2006

Citing 4
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Abstract

This paper proposes a novel denoising method for natural images by using a modified sparse coding (SC) algorithm, which is self-adaptive to the statistical property of natural images. The main idea is to utilize the shrinkage function, which is selected according to the prior distribution of sparse components, to the sparse components to remove Gaussian white noise added in an image. This denoising method is respectively evaluated by the criteria of normalized mean squared error (NMSE), Laplace mean square error (LMSE) and peak signal to noise ratio (PSNR). Compared with other denoising methods, the simulation results show that our sparse coding shrinkage technique is indeed effective and efficient.