Image Denoising Using Learned Dictionary Based on Double Sparsity Model
A novel image denoising algorithm is proposed. We introduce a new effective scheme to train a redundant dictionary from the noisy image itself. The scheme combines the double sparsity model and the zero-tree structure in the wavelet domain. The training vectors are constructed by regrouping the wavelet coefficients of high-frequency subbands in the same orientation across different scales. This scheme overcomes the limit on the input signal dimension as well as the over-fitting problem. We demonstrate the potential of this denoising algorithm with several experiments. The performance of our approach is competive to some state of the art denoising methods in some cases.
Ruihua Liang Zaixin Zhao Shengguo Li
Department of Mathematics and System Science National University of Defense Technology Changsha, 410 Department of Mathematics University of California, Berkeley CA, 94720, USA
国际会议
2011 4th International Congress on Image and Signal Processing(第四届图像与信号处理国际学术会议 CISP 2011)
上海
英文
708-712
2011-10-15（万方平台首次上网日期，不代表论文的发表时间）