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.