A New Adaptive Image Denoising Method Combining The Nonsubsampled Contourlet Transform and Total Variation

摘要：

This paper presents a new adaptive image denoising scheme by combining the nonsubsampled Contourlet transform (NSCT) and total variation model. The original image is first decomposed using NSCT.Then the mean squared error (MSE) is estimated based on Steins unbiased risk estimation(SURE). The noise of each decomposed subband is reduced using the linear adaptive threshold function, which can be constructed based on the MSE producing the preliminary primary denoised image after reconstruction. Then the preliminary primary denoised image is further filtered using the total variation model, producing the final denoised image. Experiments show that the proposed scheme can remove the pseudo-Gibbs artifacts and image noise effectively. Besides, it outperforms the existing schemes in regard of both the peak-signal-to-noise-ratio (PSNR) and the edge preservation ability.

关键词： Image processing Adaptive Image denoising NSCT SURE

作者: Xiaoyue Wu Baolong Guo Shengli Qu Zhuo Wang

作者单位: School of Electro-mechanical Engineering Xidian University Xian,China Shijiazhuang Army Command College China P.L.A.Shijiazhuang,China

会议类型: 国际会议

会议名称: The Fifth International Conference on Information Assurance and Security(第五届信息保障与安全国际会议)

会议地点: 西安

会议语种:英文

页码: 581-584

在线出版日期: 2009-08-18（万方平台首次上网日期，不代表论文的发表时间）