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Image despeckling and deblurring via regularized complex diffusion

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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.

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Notes

  1. PSNR and PFOM are evaluated for other noise variance values as well and the result is observed to follow the similar pattern.

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Authors and Affiliations

  1. Department of Mathematical and Computational Sciences, National institute of Technology, Karnataka, Surathkal, Mangaluru, 575025, India

    P. Jidesh

  2. Department of Electronics and Communications Engineering, St. Joseph Engineering College, Vamanjoor, Mangaluru, 575028, India

    A. A. Bini

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  1. P. Jidesh
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  2. A. A. Bini
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Correspondence to P. Jidesh.

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Jidesh, P., Bini, A.A. Image despeckling and deblurring via regularized complex diffusion. SIViP 11, 977–984 (2017). https://doi.org/10.1007/s11760-016-1047-6

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  • DOI: https://doi.org/10.1007/s11760-016-1047-6

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