基于图像局域特性的自适应贝叶斯小波收缩去噪算法(英文)

Adaptive Bayesian Wavelet Shrinkage Denoising Based on the Neighbourhood Characteristic

在小波去噪过程中,对小波系数进行统计建模,去噪效果会得到较大的改进.该文在贝叶斯萎缩去噪的基础上,提出了基于图像局域特性自适应阈值去噪算法.实验结果表明:新的算法比传统的算法能更有效地去除噪声,获得更高的PSNR.同时,图像中的边缘也保护得更好.

The performance of image denoising algorithms using wavelet shrinkage can be improved significantly by taking into account the statistical dependencies among wavelet coefficients. In this paper, based on the BayesShrink denoising, we propose a new intra-scale adaptive thresholding algorithm. Experiment results show the new algorithm is superior to the classical wavelet shrinkage methods, receives higher PSNR and gets better edge preservation.