An Adaptive Algorithm for Image De-Noising Based on Fuzzy Gibbs Random Fields

摘要：

Because of the flexible cliques and effective prior models, Gibbs Random Field (GRF) has gained more and more attentions in image processing. However, in those GRF-based image denoising algorithms, Gibbs distribution binary potential clique parameter, β, cant be changed adaptively with different area features when we adopt fuzzy Gibbs random field for image de-noising. The article shows an adaptive algorithm to alter the value of β. The approach can automatically decrease β to keep details near the object edges and increase β to suppress noises in smooth areas. Based on several simulation cases, the proposed adaptive algorithm is compared with the standard GRF algorithm, and the results show that the new algorithm behaves better in identifying and resolving capability.

作者: Du Xinyu Li Yongjie Yao Dezhong

作者单位: School of Life Science & Technology University of Electronic Science and Technology of China (UESTC) Chengdu, PR China

会议类型: 国际会议

会议名称: 2006 International Conference on Communications,Circuits and Systems(第四届国际通信、电路与系统学术会议)

会议地点: 广西桂林

会议语种:英文

页码: 467-470

在线出版日期: 2006-06-25（万方平台首次上网日期，不代表论文的发表时间）