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Image Denoising Via Sparse and Redundant Representations Over Learned Dictionaries in Wavelet Domain

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2 Author(s)
Huibin Li ; Sch. of Sci., Dept. of Inf. & Comput. Sci., Xi''an Jiaotong Univ., Xi''an, China ; Feng Liu

This paper proposes a novel hybrid image denoising method based on wavelet transform and sparse and redundant representations model which is called signal-scale wavelet K-SVD algorithm (SWK-SVD). In wavelet domain, mutiscale features of images and sparse prior of wavelet coefficients are achieved in a natural way. This gives us the motivation to build sparse representations in wavelet domain. Using K-SVD algorithm, we obtain adaptive and over-complete dictionaries by learning on image approximation and high-frequency wavelet coefficients respectively. This leads to a state-of-art denoising performance both in PSNR and visual effects with strong noise.

Published in:

Image and Graphics, 2009. ICIG '09. Fifth International Conference on

Date of Conference:

20-23 Sept. 2009