Abstract
The network flow optimization approach is offered for restoration
of gray-scale and color images corrupted by noise. The Ising
models are used as a statistical background of the proposed
method. We present the new multiresolution network flow minimum
cut algorithm, which is especially efficient in identification of
the maximum a posteriori (MAP) estimates of corrupted images. The
algorithm is able to compute the MAP estimates of large-size
images and can be used in a concurrent mode. We also consider the
problem of integer minimization of two functions,