Wavelet-based statistical signal processing using hidden Markovmodels
IEEE Transactions on Signal Processing
Improved hidden Markov models in the wavelet-domain
IEEE Transactions on Signal Processing
Embedded image coding using zerotrees of wavelet coefficients
IEEE Transactions on Signal Processing
Analysis of multiresolution image denoising schemes using generalized Gaussian and complexity priors
IEEE Transactions on Information Theory
Wavelet-based image denoising using a Markov random field a priori model
IEEE Transactions on Image Processing
Spatially adaptive wavelet thresholding with context modeling for image denoising
IEEE Transactions on Image Processing
Bayesian tree-structured image modeling using wavelet-domain hidden Markov models
IEEE Transactions on Image Processing
IEEE Transactions on Image Processing
A joint inter- and intrascale statistical model for Bayesian wavelet based image denoising
IEEE Transactions on Image Processing
Image denoising using scale mixtures of Gaussians in the wavelet domain
IEEE Transactions on Image Processing
IEEE Transactions on Image Processing
Directional multiscale modeling of images using the contourlet transform
IEEE Transactions on Image Processing
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This paper presents an empirical study of joint wavelet statistics for textures and other imagery to find an efficient correlation neighborhood. Since there is an established realization that modeling wavelet and other x-let coefficient relationships is crucial to any successful transform domain algorithm (such as Hidden Markov Trees), new works have been devoted to examine these dependencies from different aspects and propose an appropriate model. Because the time and computation complexity involved both in analyzing non-linear dependencies and in solving dependent models may restrict us to consider only a very small subset of contributing neighbors we focus our attention on linear dependencies (correlations) while having a squint on non-linear relations too. In this process, we study a collection of 5000 real images to corroborate our statistical analysis of the joint coefficient behavior and try to find an efficient and at the same time frugal relation map through different statistical means. The statistical observations are then certified by a coefficient significance measure and the competitiveness of the map is substantiated by plugging it into two dependent denoising frameworks.