Combined image compression and denoising using wavelets
Image Communication
Image denoising based on hierarchical Markov random field
Pattern Recognition Letters
A spatially-constrained normalized Gamma process prior
Expert Systems with Applications: An International Journal
Computer Vision and Image Understanding
Margin-maximizing classification of sequential data with infinitely-long temporal dependencies
Expert Systems with Applications: An International Journal
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A Markov random field (MRF) model-based EM (expectation-maximization) procedure for simultaneously estimating the degradation model and restoring the image is described. The MRF is a coupled one which provides continuity (inside regions of smooth gray tones) and discontinuity (at region boundaries) constraints for the restoration problem which is, in general, ill posed. The computational difficulty associated with the EM procedure for MRFs is resolved by using the mean field theory from statistical mechanics. An orthonormal blur decomposition is used to reduce the chances of undesirable locally optimal estimates. Experimental results on synthetic and real-world images show that this approach provides good blur estimates and restored images. The restored images are comparable to those obtained by a Wiener filter in mean-square error, but are most visually pleasing