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|Title:||A fourth-order Partial Differential Equation model for multiplicative noise removal in images|
|Citation:||Proceedings - 2013 International Conference on Emerging Trends in Communication, Control, Signal Processing and Computing Applications, IEEE-C2SPCA 2013, 2013, Vol., , pp.-|
|Abstract:||In coherent imaging, the sensed images are usually corrupted with multiplicative data dependent noise. Unlike additive noise, the presence of multiplicative noise destroys the information content in the original image to a great extent. In this paper, we propose a new fourth-order Partial Differential Equation (PDE) model with a noise adaptive fidelity term for multiplicative Gamma noise removal under the variational Regularization framework. Variational approaches for multiplicative noise removal generally consist of a maximum a posteriori (MAP) based fidelity term and a Total-Variation (TV) regularization term. However, the second-order TV diffusion approximates the observed images with piecewise constant images, leading to the so-called block effect. The proposed model removes the multiplicative noise effectively and approximates observed images with planar ones making the restored images more natural compared to the second-order diffusion models. The proposed method is compared with the recent state-of-the art methods and the effective restoration capability of the filter is demonstrated experimentally. � 2013 IEEE.|
|Appears in Collections:||2. Conference Papers|
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