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Image Denoising Based on Adaptive Fractional Order with Improved PM Model

机译：基于改进的PM模型的自适应分数阶图像去噪。

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摘要

In order to improve the image quality, in this paper, we propose an improved PM model. In the proposed model, we introduce two novel diffusion coefficients and a residual error term and replace the integer differential operator with the fractional differential operator in the PM model. The diffusion coefficients can be used effectively for edge detection and noise removal. The residual error term can help to prevent image distortion. Fractional order differential operator has a good characteristic that it can enhance image texture information while removing image noise. Additionally, in the two new diffusion coefficients, a novel method is proposed for automatically setting parameter k, and it does not need to do any experiments to get the value of k. For the computing fractional order diffusion coefficient, we employ the discrete Fourier transform, and an iterative scheme is carried out in the frequency domain. In the proposed model, not only is the integer differential operator replaced with the fractional differential operator, but also the order of the fractional differentiation is determined adaptively with the local variance. Comparing with some existing models, the experimental results show that the proposed algorithm can not only better suppress noise, but also better preserve edge and texture information. Moreover, the running time is greatly reduced.
机译：为了提高图像质量，本文提出了一种改进的PM模型。在提出的模型中，我们引入了两个新的扩散系数和一个残差项，并在PM模型中用分数阶微分算子代替了整数微分算子。扩散系数可以有效地用于边缘检测和噪声去除。残留误差项可以帮助防止图像失真。分数阶微分算子具有良好的特性，可以在消除图像噪声的同时增强图像纹理信息。另外，在这两个新的扩散系数中，提出了一种自动设置参数k的新颖方法，无需进行任何实验即可得出k的值。为了计算分数阶扩散系数，我们采用离散傅立叶变换，并在频域中执行了迭代方案。在所提出的模型中，不仅整数微分算子被分数微分算子代替，而且分数微分的阶数根据局部方差自适应地确定。与现有模型比较，实验结果表明，该算法不仅可以较好地抑制噪声，而且可以更好地保留边缘和纹理信息。而且，运行时间大大减少。

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  • 来源
    《Mathematical Problems in Engineering》 |2018年第6期|9620754.1-9620754.11|共11页
  • 作者

    Yu Jimin; Zhi Rumeng; Zhou Shangbo; Tan Lilian;

  • 作者单位

    Chongqing Univ Posts & Telecommun, Chongqing 400065, Peoples R China;

    Chongqing Univ Posts & Telecommun, Chongqing 400065, Peoples R China;

    Chongqing Key Lab Comp Network & Commun Technol, Chongqing 400065, Peoples R China;

    Chongqing Univ Posts & Telecommun, Chongqing 400065, Peoples R China;

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