Journal
COMPUTERS & MATHEMATICS WITH APPLICATIONS
Volume 76, Issue 4, Pages 774-787Publisher
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.camwa.2018.05.018
Keywords
Image enhancement; Image denoising; Dynamic stochastic resonance; Regularization method; Partial differential equation
Categories
Funding
- National Natural Science Foundation of China [61671243, 61471199]
- Training Program of the Major Research Plan of the National Natural Science Foundation of China [91538108]
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Dynamic stochastic resonance (DSR) is a distinctive technique for enhancement of dark and low-contrast image. Noise is necessary for DSR based image enhancement and the level of noise will be enlarged simultaneously with brightness, which reduces the perceptual quality of the enhanced image greatly and also increases the difficulty of subsequent de noising because removing high level of noise often leads to serious loss of image details. In this paper, instead of removing noise after the enhancement process is complete, we propose to suppress noise gradually and simultaneously in the process of enhancement. We rewrite the traditional partial differential equation (PDE) based DSR model in variational framework firstly, and then propose a novel total variation regularized (TV) DSR method for image enhancement. The existence and uniqueness of solution of the TV regularized DSR model is proved theoretically. Moreover, we generalize the TV regularized DSR model in variational framework and in PDE framework, respectively, and therefore we can incorporate more existing denoising methods into our approach. Numerical comparisons demonstrate that the proposed technique gives significant performance in terms of contrast and brightness enhancement as well as noise suppression, and therefore can obtain enhanced image with good perceptual quality. (C) 2018 Elsevier Ltd. All rights reserved.
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