Laser speckle images research based on wavelet-domain hidden Markov models

Wavelet-domain hidden Markov models have proven to be useful tools for statistical signal and image processing. The hidden Markov tree (HMT) model captures the key features of the joint probability density of the wavelet coefficients of laser speckle images. In this paper, considering both the characteristics of laser speckle images after log-transformed and the statistical features of wavelet transformed images, a multiscale image filtering algorithm, which based on two-state hidden markov model (HMM) and mixture Gaussian statistical model, has been used to decrease the Gaussian white noise in speckle images. By using EM (Expectation Maximization) algorithm to estimate noise coefficients in each scale and to obtain power spectrum matrix, then this carried through the synchronized two Filter that are IIR(infinite impulse response) Wiener Filter and HMM, according to scale size, and achieve the experiments as well as the comparison with other denoising methods were presented at last.