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Network Flow Optimization for Restoration of Images

Boris A. Zalesky
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Abstract

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.

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Boris A. Zalesky. 2001-06-21. Network Flow Optimization for Restoration of Images. https://arxiv.org/abs/math/0106180

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