%0 Journal Article
%T An Improved Joint Scheme for Image Denoising<br>一种改进的图像组合滤波方法
%A ( Institute of Pattern Recognition &
%A Artificial Intelligence
%A Huazhong University of Science &
%A Technology
%A Laboratory for State Key Image Processing &
%A Intelligence Control
%A Wuhan ( College of Electronic Information Engineering
%A South-Central University for Nationalities
%A Wuhan
%A <br>侯建华
%A 田金文柳健
%J 光子学报
%D 2005
%I 
%X On the basis of the combined method presented in Reference7],an improvement was implemented by exploiting the characteristics of both wavelet thresholding denoising and spatial Wiener filtering.After BayesShrink thresholding denoising in wavelet domain to obtain a pre-denoised image,the noise variance was estimated for the following Lee filtering.The optimal noise variance estimation for Lee filter was given by numerical computation.Experiment results show the improvement of the proposed approach in terms of MSE,signal-to-noise ratio (SNR),as well as adaptability to different images.
%K Wavelet thresholding denoising
%K BayesShrink
%K Spatially adaptive filtering
%K Pre-denoised image
%K Noise variance<br>小波阈值去噪
%K BayesShrink
%K 空域自适应滤波
%K 预去噪图像
%K 噪声方差
%U http://www.alljournals.cn/get_abstract_url.aspx?pcid=6E709DC38FA1D09A4B578DD0906875B5B44D4D294832BB8E&cid=47EA7CFDDEBB28E0&jid=9F6139E34DAA109F9C104697BF49FC39&aid=00E1E2E0DA75DFF6&yid=2DD7160C83D0ACED&vid=339D79302DF62549&iid=708DD6B15D2464E8&sid=3C4D8AE76F4902FF&eid=8CD2A779BF9E6C32&journal_id=1004-4213&journal_name=光子学报&referenced_num=9&reference_num=13