The network flow optimization approach is offered for restoration of grayscale and color images corrupted by noise. The Ising models are used as a statistical background of the proposed method. The new multiresolution network flow minimum cut algorithm, which is especially efficient in identification of the maximum a posteriori estimates of corrupted images, is presented. The algorithm is able to compute the MAP estimates of large size images and can be used in a concurrent mode. We also describe the efficient solutions of the problem of integer minimization of two energy functions for the Ising models of gray-scale and color images.
@article{arxiv.math/0106180,
title = {Network Flow Optimization for Restoration of Images},
author = {Boris A. Zalesky},
journal= {arXiv preprint arXiv:math/0106180},
year = {2016}
}