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. © 2009 IEEE.
CITATION STYLE
Li, H., & Liu, F. (2009). Image denoising via sparse and redundant representations over learned dictionaries in wavelet domain. In Proceedings of the 5th International Conference on Image and Graphics, ICIG 2009 (pp. 754–758). IEEE Computer Society. https://doi.org/10.1109/ICIG.2009.101
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