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Solving a generalized order improved diffusion equation of image denoising using a CeNN-based scheme

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Abstract

This paper presents a novel algorithm for image denoising using an improved nonlinear diffusion PDE model and a cellular neural network (CeNN) scheme. In particular, the images corrupted with multiplicative (speckle) noise have been considered. The proposed generalized-order nonlinear diffusion (GOND) model is solved through a suitable cellular neural network (CeNN) approach. The CeNN templates act like edge-preserving filters to reduce the multiplicative noise. The present study also gives a convergence analysis of the proposed CeNN based solution scheme. Further, the proposed scheme is numerically validated on synthetic, medical, and real SAR images. The obtained results demonstrate that the proposed algorithm provides a better way to deal with speckle noise and suppresses the staircase effects. To broaden the simulation results, the proposed method is applied to images corrupted with Gaussian, Rayleigh, and gamma noise. The proposed method for SSIM values interpret 0.2dB to 0.4dB better than state-of-the-art methods and comparative results in terms of PSNR in most test cases.

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  1. https://in.mathworks.com/Matlabcentral/fileexchange/36906-detail-preserving-anosotropic-diffusion-for-speckle-filtering-dpad

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Acknowledgements

One of the authors Mahima is thankful the support of University Grant Commission (UGC) during her Ph.D through Ref. No. 412261.

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  1. Department of Mathematics, Indian Institute of Technology Roorkee, Roorkee, 247667, India

    Mahima Lakra & Sanjeev Kumar

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  1. Mahima Lakra
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  2. Sanjeev Kumar
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Correspondence to Mahima Lakra.

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Lakra, M., Kumar, S. Solving a generalized order improved diffusion equation of image denoising using a CeNN-based scheme. Multimed Tools Appl 81, 32393–32420 (2022). https://doi.org/10.1007/s11042-022-12998-7

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  • DOI: https://doi.org/10.1007/s11042-022-12998-7

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