A new application of fractional Atangana-Baleanu derivatives: Designing ABC-fractional masks in image processing
A new application of fractional Atangana-Baleanu derivatives: Designing ABC-fractional masks in image processing
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DOI：
10.1016/j.physa.2019.123516
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发表时间：
2020-03-15
影响因子：
3.3
通讯作者：
Atangana, Abdon
中科院分区：
文献类型：
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作者：
Ghanbari, Behzad;Atangana, Abdon
基于分数阶导数和分数阶积分的新定义，提出了几种用于图像去噪的分数阶掩模。在每种方法中，该过程都涉及构建一个正方形，然后将其应用于噪声图像中的所有相应块。我们已经测量了我们提出的面具，采用一些已知的指标的去噪性能。它们是峰值信噪比（PSNR）、熵和SSIM。实验结果表明，我们提出的掩模是计算效率高，其性能与其他标准和分数平滑滤波器兼容。(C)2019爱思唯尔B.V.保留所有权利。
Based on a new definition for derivative and integral of fractional-order, several fractional masks have been presented for the use of image denoising. In each method, the process involves constructing a square and then applying it to all the corresponding blocks in the noisy image. We have measured the denoising performance of our proposed masks by employing some known indexes. They are the peak signal-to-noise ratio (PSNR), ENTROPY, and SSIM. The obtained experimental results show that our proposed masks are computationally efficient, and their performances are compatible with other standard and fractional smoothing filters. (C) 2019 Elsevier B.V. All rights reserved.