Graphical statistical modeling of wavelet coefficients and its applications
Igor V. Kozintsev, Xiao Shu, Kannan Ramchandran · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2000
Wavelet transform-based methods are currently used in a variety of image and video processing applications and are popular candidates for future image and video processing standards. Very little, however, has been done to develop efficient and simple stochastic models for wavelet image data. In this paper we review some existing modeling approaches for wavelet image data. Inspired by our recent estimation-quantization image coder, we introduce an efficient graphical stochastic model for wavelet image coefficients. Specifically, we propose to model wavelet image coefficients as Gaussian random variables with parameters determined by an underlying hidden Markov-type process. This stochastic model is defined using a factor graph framework. We test our model for denoising images corrupted by additive Gaussian noise. Our results are among the state-of-the-art in the field and they indicate the promise of the proposed model.