Markov Random Field Model-Based Salt and Pepper Noise Removal
This work addresses image denoising for applications like photography or medical imaging, but it is incremental as it applies existing MRF and graph cut methods to a well-studied noise type.
The paper tackled salt and pepper noise removal in images by formulating it as an inpainting problem using Markov Random Field models with smoothness priors, and found that graph cuts with α-expansion moves outperformed other minimization techniques in terms of PSNR and computational speed.
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 $α$-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 $α$-expansion moves over the other techniques both in terms of PSNR and also computational times.