Image Enhancement under Data-Dependent Multiplicative Gamma Noise
Joint Authors
Pacheeripadikkal, Jidesh
Anattu, Bini
Source
Applied Computational Intelligence and Soft Computing
Issue
Vol. 2014, Issue 2014 (31 Dec. 2014), pp.1-8, 8 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2014-06-01
Country of Publication
Egypt
No. of Pages
8
Main Subjects
Information Technology and Computer Science
Abstract EN
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.
American Psychological Association (APA)
Pacheeripadikkal, Jidesh& Anattu, Bini. 2014. Image Enhancement under Data-Dependent Multiplicative Gamma Noise. Applied Computational Intelligence and Soft Computing،Vol. 2014, no. 2014, pp.1-8.
https://search.emarefa.net/detail/BIM-513350
Modern Language Association (MLA)
Pacheeripadikkal, Jidesh& Anattu, Bini. Image Enhancement under Data-Dependent Multiplicative Gamma Noise. Applied Computational Intelligence and Soft Computing No. 2014 (2014), pp.1-8.
https://search.emarefa.net/detail/BIM-513350
American Medical Association (AMA)
Pacheeripadikkal, Jidesh& Anattu, Bini. Image Enhancement under Data-Dependent Multiplicative Gamma Noise. Applied Computational Intelligence and Soft Computing. 2014. Vol. 2014, no. 2014, pp.1-8.
https://search.emarefa.net/detail/BIM-513350
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-513350