Real-time restoration and segmentation algorithms for hidden Markov mesh random fields image models

This paper addresses the image restoration and segmentation problems under the assumption that images can be represented by hidden Markov mesh random fields models. We outline coherent approaches to both the problems of image segmentation and restoration (pixel labeling) and model acquisition (learning). We exhibit a real-time labeling algorithm for a 3rd order Markov mesh which achieves minimal complexity. We develop a learning technique which permits to estimate the model parameters without ground truth information. We display experimental results which demonstrate that the approach is subjectively relevant to the image restoration and segmentation problems.

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