Mixed Image Denoising Method of Non-local Means and Adaptive Bayesian Threshold Estimation in NSCT Domain

摘要：

Image denoising is an important task inside the image processing area, a mixed image denoising method based on non-local means (NL-means) and adaptive bayesian threshold estimation in nonsubsampled contourlet transform (NSCT) is proposed. In this algorithm, first we remove the noise using NL-means method in spatial domain, then the denoised image using NL-means method is decomposed by NSCT into a low frequency subband and a set of multiscale and multidirectional high frequency subbands. The high frequency coefficients are estimated by the minimizing Bayesian risk. then the denoising image is gotten by performing the inverse NSCT to these estimated coefficents. Experimental results show that the proposed method indeed removes noise significantly and retains most image edges. The results compare favorably with the reported results in the recent denoising literature.

关键词： Image Denoising Non-local Means Nonsubsampled Contourlet Transform Bayesian Estimation

作者: Qian ZHAO Bo YE Xiaohua WANG Duo ZHOU

作者单位: Dept.of Electronic Science and Technology Shanghai University of Electric Power Shanghai China

会议类型: 国际会议

会议名称: 2010 3rd IEEE International Conference on Computer Science and Information Technology(第三届IEEE计算机科学与信息技术国际会议 ICCSIT 2010)

会议地点: 成都

会议语种:英文

页码: 636-639

在线出版日期: 2010-07-07（万方平台首次上网日期，不代表论文的发表时间）