Please use this identifier to cite or link to this item: https://idr.nitk.ac.in/jspui/handle/123456789/11522
Title: Image despeckling and deblurring via regularized complex diffusion
Authors: Jidesh, P.
Bini, A.A.
Issue Date: 2017
Citation: Signal, Image and Video Processing, 2017, Vol.11, 6, pp.977-984
Abstract: In this paper an image restoration and enhancement model is being proposed, which is suitable for multiplicative data-dependent speckle noise (whose intensity is Gamma distributed) under linear shift-invariant blurring artifacts. The proposed strategy devises a nonlinear second-order diffusive-reactive model for enhancing and restoring images degraded by the aforementioned scenario. The reactive term is derived based on the Maximum a posteriori (MAP) estimator, to make it adaptive to the noise distribution in the input data. This noise-adaptive reactive term helps to restore and enhance the images under data-correlated noise setup. Unlike the other second-order nonlinear diffusion methods, the proposed solution preserves edges and details and reduces piecewise constant approximation in the homogeneous intensity regions in the course of its evolution. The experimental results demonstrated in this paper duly support the above claims. 2017, Springer-Verlag London.
URI: http://idr.nitk.ac.in/jspui/handle/123456789/11522
Appears in Collections:1. Journal Articles

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