摘要
采用双树复数小波变换对图像进行分解与重构,在BayesShrink阈值去噪的基础上,提出了基于小波系数层内和层间局域特性的自适应阈值去噪算法;构造出具有层内和层间局域特性的统计量和相应的映射,产生新的BayesShrink阈值.实验表明本方法能有效地去除图像中的白噪声,同时还能较好地保留图像的边缘信息,其效果优于目前的一些小波去噪方法.
We use dual- tree complex wavelet transform to decompose and reconstruct an image. Based on BayesShrink denoising, we propose a new intra- scale and inter- scale adaptive thresholding algorithm. We construct a statistics and the corresponding mapping to produce a new BayesShrink threshold. Experiments show that the proposed method is better than recently published denoising methods, because the proposed method removes white noise more effectually and gets better edge preservation.
出处
《福州大学学报（自然科学版）》
CAS
CSCD
北大核心
2008年第1期55-58,共4页
Journal of Fuzhou University(Natural Science Edition)
基金
福建省自然科学基金资助项目(A0510005)
