Improvements on sparse coding shrinkage and contourlet transform for image denosing

Authors:
Yu Xin-hua;Zhang Fu-ming;Wang Zhan-qing;Ye Fu-dong
Affiliations:
School of Science, Wuhan University of Technology, Wuhan, China;School of Science, Wuhan University of Technology, Wuhan, China;School of Science, Wuhan University of Technology, Wuhan, China;Technology Center Hubei Ecology Vocational College, Wuhan, China
Venue:
WiCOM'09 Proceedings of the 5th International Conference on Wireless communications, networking and mobile computing
Year:
2009

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Abstract

In this work, according to the disadvantages of sparse coding shrinkage and Contourlet transform, we investigated the use of sparse coding shrinkage in conjunction with Contourlet transform for denoising image data and introduced a new image denoising algorithm. The new algorithm based on linear noise model and excellently solves the denosing of image that contains additive noise with unknown variance. Experimental results show that this new algorithm is indeed effective and efficient. Compared with other denoisng methods, the algorithm is much better for it enhances the value of SNR, reduces the value of MSE, and obtains a better quality of image reconstruction.