摘要
提出了一种基于D-S证据理论的小波萎缩图像去噪方法。对含噪图像进行离散平稳小波变换后,运用Bayes方法分得各层高频子带的小波萎缩系数,根据小波萎缩系数的空间及层间相关性,利用D-S证据理论的合成法则对初始小波萎缩系数进行融合修正。实验结果表明,该方法在有效地去除图像中的噪声的同时,还能较好地保留图像的边缘信息。算法在性能指标和视觉质量上均优于Donoho的小波软阈值去噪方法、传统的中值滤波法和Winner滤波法。
A new method for image de-noising by wavelet shrinkage based on evidence theory was given. In the method, a noise image was multi-scale decomposed by discrete stationary wavelet transform. Bayes method was used to gain original wavelet shrinkage factors of high frequency subbands. According to scale and space consistency of original wavelet shrinkage factors, wavelet shrinkage factors were modified by fusion rules of D-S evidence theory. The experiment result shows that new method can not only effectively get rid of noise but also preserve edges of image well. As to performancy and visual quality, the algorithm is better than the wavelet soft-threshholding given by Donoho, the traditional midian value fliting method and winner fliting method.
出处
《光学技术》
EI
CAS
CSCD
北大核心
2005年第5期713-716,共4页
Optical Technique
关键词
D-S证据理论
图像去噪
平稳小波变换
D-Sevidence theory
image denoising
discrete stationary wavelet transform
作者简介
杨海峰（1974-），男，河北人，北京理工大学博士研究生，从事图像处理和数据融合研究。E-mail：yhf1974@bit．edu．cn.
