Iterative Shrinkage Thresholding Algorithm with Redundant Dictionary for Image Denoising
Upon the state-of-art technique of image sparse reconstruction, a new image denoising algorithm based on l1 norm model is proposed in this paper. Without using the common transform bases, firstly the redundant dictionary trained by K-SVD algorithm is used as sparse representation for different image models. Then followed by the denoising algorithm consist of fast iterative shrinkage thresholding algorithm and least squares solution. The simulation results for both the current l0norm model based method and the proposed method demonstrate our method is more robust than the current method in terms of the peak signal-to-noise ratio.
sparse representation redundant dictionary iterative shrinkage threshoding image denoising
YuLu Huahua Chen
School of Telecommunication Engineering Hangzhou Dianzi University Hangzhou, China
国际会议
上海
英文
347-350
2011-10-15（万方平台首次上网日期，不代表论文的发表时间）