A Review of Multiscale Statistical Image Models
图像多尺度统计模型综述

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.