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Investigation of key parameters to define stop condition in image denoising algorithms based on the diffusion equation

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Abstract

The first use of diffusion equation for denoising images was first proposed back 1990. Although such an approach showed a remarkable performance, there still is quite a high amount of impact noise present in the image. To address this and other issues in the proposed approach and to improve the performance of such denoising methods, over the years different studies have used smoothing filters, wavelet transform coefficients, and adaptive parameters. In this paper, we first investigate the effects of different approaches in diffusion-based algorithms and then introduced a comprehensive formula. Furthermore, through the use of a new proposed convergence curve that is only based on the noisy image. So, we determine the stoppage point in the algorithm which would lead in preventing the introduction of any additional artifact such as blurring in the denoising process. The results show that compared to the use of wavelet transform and adaptive parameters with high computational complexity the use of smoothers is a more effective approach.

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Author notes

  1. Fatemeh Sabaghian, Farah Torkamani Azar these authors contributed equally to this work.

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  1. Faculty of Electrical Engineering, Shahid Beheshti University, 19439, Velnjak, Tehran, Iran

    Fatemeh Sabaghian & Farah Torkamani-Azar

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  1. Fatemeh Sabaghian
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  2. Farah Torkamani-Azar
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Correspondence to Farah Torkamani-Azar.

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Sabaghian, F., Torkamani-Azar, F. Investigation of key parameters to define stop condition in image denoising algorithms based on the diffusion equation. SIViP 17, 1011–1017 (2023). https://doi.org/10.1007/s11760-022-02306-z

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  • DOI: https://doi.org/10.1007/s11760-022-02306-z

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