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首页> 外文期刊>Mathematical Problems in Engineering >Image Structure-Preserving Denoising Based on Difference Curvature Driven Fractional Nonlinear Diffusion
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Image Structure-Preserving Denoising Based on Difference Curvature Driven Fractional Nonlinear Diffusion

机译：基于差分曲率驱动的分数阶非线性扩散的图像保持图像降噪

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摘要

The traditional integer-order partial differential equations and gradient regularization based image denoising techniques often suffer from staircase effect, speckle artifacts, and the loss of image contrast and texture details. To address these issues, in this paper, a difference curvature driven fractional anisotropic diffusion for image noise removal is presented, which uses two new techniques, fractional calculus and difference curvature, to describe the intensity variations in images. The fractional-order derivatives information of an image can deal well with the textures of the image and achieve a good tradeoff between eliminating speckle artifacts and restraining staircase effect. The difference curvature constructed by the second order derivatives along the direction of gradient of an image and perpendicular to the gradient can effectively distinguish between ramps and edges. Fourier transform technique is also proposed to compute the fractional-order derivative. Experimental results demonstrate that the proposed denoising model can avoid speckle artifacts and staircase effect and preserve important features such as curvy edges, straight edges, ramps, corners, and textures. They are obviously superior to those of traditional integral based methods. The experimental results also reveal that our proposed model yields a good visual effect and better values of MSSIM and PSNR.
机译：传统的整数阶偏微分方程和基于梯度正则化的图像去噪技术通常会遭受阶梯效应，斑点伪像以及图像对比度和纹理细节的损失。为了解决这些问题，本文提出了一种由差曲率驱动的分数各向异性扩散来去除图像噪声的方法，该方法使用分数微积分和差曲率这两种新技术来描述图像的强度变化。图像的分数阶导数信息可以很好地处理图像的纹理，并在消除斑点伪影和抑制阶梯效应之间取得良好的折衷。由二阶导数沿着图像的梯度方向并垂直于该梯度构造的差曲率可以有效地区分斜坡和边缘。还提出了傅里叶变换技术来计算分数阶导数。实验结果表明，所提出的去噪模型可以避免斑点斑点和阶梯效应，并保留重要的特征，例如弯曲的边缘，笔直的边缘，斜坡，拐角和纹理。它们显然优于传统的基于积分的方法。实验结果还表明，我们提出的模型产生了良好的视觉效果以及更好的MSSIM和PSNR值。

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  • 来源
    《Mathematical Problems in Engineering》 |2015年第6期|930984.1-930984.16|共16页
  • 作者

    Yin Xuehui; Zhou Shangbo;

  • 作者单位

    Chongqing Univ, Coll Comp Sci, Chongqing 400030, Peoples R China.;

    Chongqing Univ, Coll Comp Sci, Chongqing 400030, Peoples R China.;

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