BayesShrink是小波收缩降噪最好的算法之一，而WienerChop方法则是利用小波域维纳滤波改进了VisuShrink算法。为了更好地滤除噪声，研究了WienerChop组合BayesShrink进行降噪的方法。实验表明，该组合算法优于WienerChop和BayesShrink算法，其可产生更低的均方误差和更高的信噪比。它不仅综合了WienerChop和BayesShrink两种算法的优点，而且改善了WienerChop算法的过光滑和BayesShrink算法残留较多噪声的问题，同时可获得视觉上更为愉悦的降噪图像。

BayesShrink is one of the best algorithms for wavelet thresholding denoising, while WienerChop improves VisuShrink by Wiener filtering in wavelet domain. We studied the denoising method uniting BayesShrink and WienerChop. The combined algorithm has smaller mean squared erroe(MSE) and higher signal to noise ratio(SNR) than BayesShrink or WienerChop. It integrates the advantages of the two algorithms, and improves the problems which images are smoothed overly by WienerChop and BayesShrink retains some noise artifacts. It can visually obtain more pleasing denoised images.