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Applied Computational Intelligence and Soft Computing
Volume 2014 (2014), Article ID 981932, 8 pages
Research Article

Image Enhancement under Data-Dependent Multiplicative Gamma Noise

1Department of Mathematical and Computational Sciences, National Institute of Technology, Karnataka 575025, India
2Department of Electronics and Communications Engineering, National Institute of Technology, Karnataka 575025, India

Received 13 February 2014; Accepted 19 May 2014; Published 1 June 2014

Academic Editor: Christian W. Dawson

Copyright © 2014 Jidesh Pacheeripadikkal and Bini Anattu. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


An edge enhancement filter is proposed for denoising and enhancing images corrupted with data-dependent noise which is observed to follow a Gamma distribution. The filter is equipped with three terms designed to perform three different tasks. The first term is an anisotropic diffusion term which is derived from a locally adaptive p-laplacian functional. The second term is an enhancement term or a shock term which imparts a shock effect at the edge points making them sharp. The third term is a reactive term which is derived based on the maximum a posteriori (MAP) estimator and this term helps the diffusive term to perform a Gamma distributive data-dependent multiplicative noise removal from images. And moreover, this reactive term ensures that deviation of the restored image from the original one is minimum. This proposed filter is compared with the state-of-the-art restoration models proposed for data-dependent multiplicative noise.