SVM-based Filter Using Evidence Theory and Neural Network for Image Denosing

• 作者: Tzu-Chao Lin
• 来源期刊: 软件工程与应用(英文)
• 学科: 工学
• 关键词: NEURAL Network EVIDENCE Theory IMPULSE Noise IMAGE RESTORATION
• 年，卷（期）: 2013,（3）
• 页码范围: 106-110
• 页数: 5页
• 分类号: TP39

摘要

This paper presents a novel decision-based fuzzy filter? based on support vector machines and Dempster-Shafer? evidence theory for effective noise suppression and detail preservation. The proposed filter uses an SVM impulse detector to judge whether an input pixel is noisy. Sources of evidence are extracted, and then the fusion of evidence based on the evidence theory provides a feature vector that is used as the input data of the proposed SVM impulse detector. A fuzzy filtering mechanism, where the weights are constructed using a counter-propagation neural network, is employed. Experimental results shows that the proposed filter has better performance in terms of noise suppression and detail preservation.