%0 Journal Article
%T A Review of Multiscale Statistical Image Models<br>图像多尺度统计模型综述
%A WANG Wen
%A RUI Guo-sheng
%A WANG Xiao-dong
%A XING Fu-cheng
%A <br>王文
%A 芮国胜
%A 王晓东
%A 邢福成
%J 中国图象图形学报
%D 2007
%I 
%X The algorithms based on wavelet transform have been very popular in image processing applications such as image compression, denoising, segmentation, texture analysis and synthesis. Multiscale statistical models for image characteristic are the key problems for these applications. This paper reviewed the statistical models for images in wavelet domain. Firstly, the marginal models for non-Gaussian distribution of image wavelet coefficients were studied, then the dependency models including interscale, intrascale and composite dependencies were analyzed, and the paper indicated the advantages and disadvantages of the models and gave normalized measures for the abilities of different dependency models to capture the dependencies between coefficients. At last, image statistical models based on multiscale geometric analysis were introduced in brief, and the possible future work is pointed out.
%K wavelet
%K multiscale statistical model
%K non-Gaussian
%K dependency model
%K mutual information
%K multiscale geometric analysis<br>小波
%K 多尺度统计模型
%K 非高斯
%K 相关模型
%K 互信息
%K 多尺度几何分析
%U http://www.alljournals.cn/get_abstract_url.aspx?pcid=5B3AB970F71A803DEACDC0559115BFCF0A068CD97DD29835&cid=8240383F08CE46C8B05036380D75B607&jid=D06194629680C940ACE75262F54B9D85&aid=BD03BA3DCD1616E4&yid=A732AF04DDA03BB3&vid=59906B3B2830C2C5&iid=B31275AF3241DB2D&sid=6D947E6CDDEFFBDE&eid=CF2C3194F1B66D28&journal_id=1006-8961&journal_name=中国图象图形学报&referenced_num=0&reference_num=52