Image Enhancement under Data-Dependent Multiplicative Gamma Noise. (1st June 2014)

• Record Type: Journal Article
• Authors: Pacheeripadikkal, Jidesh; Anattu, Bini
• Other Names: Dawson Christian W. Academic Editor.
• Is Part Of: Applied computational intelligence and soft computing. Volume 2014(2014)
• Publication Date: 2014-06-01
• Subjects: Computational intelligence -- Periodicals; Soft computing -- Periodicals; 006.305
• DOI: 10.1155/2014/981932
• Languages: English
• ISSNs: 1687-9724

Abstract

Abstract : 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.