Image Enhancement under Data-Dependent Multiplicative Gamma Noise
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...
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| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2014-01-01
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| Series: | Applied Computational Intelligence and Soft Computing |
| Online Access: | http://dx.doi.org/10.1155/2014/981932 |
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| Summary: | 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. |
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| ISSN: | 1687-9724 1687-9732 |