  Problem of impulse noise reduction is a very well studied problem in image
processing community and many different approaches have been proposed to tackle
this problem. In the current work, the problem of fixed value impulse noise
(salt and pepper) removal from images is investigated by use of a Markov Random
Field (MRF) models with smoothness priors. After the formulation of the problem
as an inpainting problem, graph cuts with $\alpha$-expansion moves are
considered for minimization of the energy functional. As for comparisons,
several other minimization techniques that are widely used for MRF models'
optimization are considered and the results are compared using
Peak-Signal-to-Noise-Ratio (PSNR) and Structural Similarity Index (SSIM) as
metrics. The investigations show the superiority of graph cuts with
$\alpha$-expansion moves over the other techniques both in terms of PSNR and
also computational times.
