Image Denoising with Grouplet Transform

摘要：

Grouplet transform(GT) can take advautage of the images geometry structure since the bases of Grouplet can adapt the different geometry structure in different scales. The association fields that cakulate by The Block Matching algorithm whicb cannot adaptive to different textures cannot follow the turbulent texture contained in an image. Grouplet transform based on Streamline (GTS) introduced srreamline to improve the performance of represent of turbulent texture. The starting pixel selected for association fields pruning was arbitrary, and one flow will prune to several flows that would destroy the original texture decreased the performance of Grouplet Transform. This paper proposed an advanced grouplet transform (AGT) that make use of the advantage of Greedy algorithm and Dynamic Programming algorithm in association fields pruning to ensure association fields well suited of the images texture structure. Experimental results show that the performance of image denoising by AGTthreshold outperforms GT-threshold denoising method and GTS-threshold denoising method.

关键词： Grouplet transform Stream Greedy algorithm Dynamic programming algorithm

作者: Jingwen Yan Zhixi Wang Longquan Dai Daxiang Huang

作者单位: Department of Electronic Engineering Shantou University,Shantou Guangdong,China

会议类型: 国际会议

会议名称: The 2nd IEEE International Conference on Advanced Computer Control(第二届先进计算机控制国际会议 ICACC 2010)

会议地点: 沈阳

会议语种:英文

页码: 65-69

在线出版日期: 2010-03-27（万方平台首次上网日期，不代表论文的发表时间）