A Bayesian super-resolution approach to demosaicing of blurred images
EURASIP Journal on Applied Signal Processing
Super-resolution using hidden Markov model and Bayesian detection estimation framework
EURASIP Journal on Applied Signal Processing
Efficient recursive multichannel blind image restoration
EURASIP Journal on Applied Signal Processing
MR Brain Tissue Classification Using an Edge-Preserving Spatially Variant Bayesian Mixture Model
MICCAI '08 Proceedings of the 11th international conference on Medical Image Computing and Computer-Assisted Intervention - Part I
Validation of classical and blind criteria for image quality evaluation
SIP '07 Proceedings of the Ninth IASTED International Conference on Signal and Image Processing
L1 prior majorization in Bayesian image restoration
DSP'09 Proceedings of the 16th international conference on Digital Signal Processing
Spatially Varying Mixtures Incorporating Line Processes for Image Segmentation
Journal of Mathematical Imaging and Vision
Edge-preserving Bayesian image superresolution based on compound Markov random fields
ICANN'07 Proceedings of the 17th international conference on Artificial neural networks
Transmission tomography reconstruction using compound gauss-markov random fields and ordered subsets
ICIAR'06 Proceedings of the Third international conference on Image Analysis and Recognition - Volume Part II
Color image deblurring with impulsive noise
VLSM'05 Proceedings of the Third international conference on Variational, Geometric, and Level Set Methods in Computer Vision
Bayesian reconstruction of color images acquired with a single CCD
IbPRIA'05 Proceedings of the Second Iberian conference on Pattern Recognition and Image Analysis - Volume Part I
A plausible texture enlargement and editing compound Markovian model
MUSCLE'11 Proceedings of the 2011 international conference on Computational Intelligence for Multimedia Understanding
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We develop a multichannel image restoration algorithm using compound Gauss-Markov random fields (CGMRF) models. The line process in the CGMRF allows the channels to share important information regarding the objects present in the scene. In order to estimate the underlying multichannel image, two new iterative algorithms are presented and their convergence is established. They can be considered as extensions of the classical simulated annealing and iterative conditional methods. Experimental results with color images demonstrate the effectiveness of the proposed approaches.