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Xiaojun Yu
Jiangsu University of Technology
Computer Science & IT

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A Sparse Representation Image Denoising Method Based on Orthogonal Matching Pursuit Xiaojun Yu; Defa Hu
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 13, No 4: December 2015
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v13i4.1901

Abstract

Image denoising is an important research aspect in the field of digital image processing, and sparse representation theory is also one of the research focuses in recent years. The sparse representation of the image can better extract the nature of the image, and use a way as concise as possible to express the image. In image denoising based on sparse representation, the useful information of the image possess certain structural features, which match the atom structure. However, noise does not possess such property, therefore, sparse representation can effectively separate the useful information from noise to achieve the purpose of denoising. Aiming at image denoising problem of low signal-to-noise ratio (SNR) image, combined with Orthogonal Matching Pursuit and sparse representation theory, this paper puts forward an image denoising method. The experiment shows that compared with the traditional image denoising based on Symlets, image denoising based on Contourlet transform, this method can delete noise in low SNR image and keep the useful information in the original image more efficiently.
Co-Authors Defa Hu