Modified Laplacian Scale Mixture Modeling and Nonlocal Low Rank approximation (MLSM-NLR)

Abstract

Reduction of mixed noise is an ill posed problem for the occurrence of contrasting distributions of noise in the image. The mixed noise that is usually encountered is the simultaneous presence of additive white Gaussian noise (AWGN) and impulse noise (IN). A standard approach to denoise an image with such corruption is to apply a rank order filter (ROF) followed by an efficient linear filter to remove the residual noise. However, ROF cannot completely remove the heavy tail of the noise distribution originating from the IN and thus the denoising performance can be suboptimal. In this paper, we present a variational step to remove the heavy tail of the noise distribution. Through experiments, it is shown that this approach can significantly improve the denoising performance of mixed AWGN-IN using well-established methods.

                
    @InProceedings{Tariqul-2018,
    author  = {Islam, Mohammad Tariqul and Saha, Dipayan and Rahman, S. M. Mahbubur and Ahmad, M.
    Omair and Swamy, M. N. S.},
    booktitle = {Int. Conf. on Electrical and Computer Engineering (ICECE)},
    title   = {A Variational Step for Reduction of Mixed Gaussian-Impulse Noise from Images},
    pages   = {97-100},
    year    = {2018},
    }