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Decision-Based Marginal Diffusion for Salt-and-Pepper Noise Removal in Color Images
Journal of Circuits, Systems and Computers ( IF 1.5 ) Pub Date : 2020-12-16 , DOI: 10.1142/s021812662150153x Hongyao Deng 1 , Xiuli Song 2 , Huilian Fan 1
Journal of Circuits, Systems and Computers ( IF 1.5 ) Pub Date : 2020-12-16 , DOI: 10.1142/s021812662150153x Hongyao Deng 1 , Xiuli Song 2 , Huilian Fan 1
Affiliation
Salt-and-pepper noise suppression for vector-valued images usually employs vector median filtering, total variation L1 model, diffusion methods and variants. These approaches, however, often introduce excessive smoothing and can result in extensive visual feature blurring and are suitable only for images with low intensity noise. In this paper, a new method, as an important preprocessing step in cyber-physical systems, is presented to suppress salt-and-pepper noise that can overcomes this limitation. This method first detects the corrupted pixels and then restores them using channel-wise anisotropic diffusion. The means is twofold. On the one hand, the marginal approach is used to perform noise suppression separately in each channel because the contaminative pixel components are of independent distribution. On the other hand, a decision-based anisotropic diffusion method is applied to each channel to restores them. The anisotropic diffusion is an energy-dissipating process with time, and dependent on geometric analysis of shape of the energy surface. Simulation results indicate that the proposed method for impulsive noise removal achieves the state-of-the-arts results.
中文翻译:
彩色图像中椒盐噪声去除的基于决策的边际扩散
向量值图像的椒盐噪声抑制通常采用向量中值滤波、总变差 L1 模型、扩散方法和变体。然而,这些方法通常会引入过度平滑,并可能导致广泛的视觉特征模糊,并且仅适用于具有低强度噪声的图像。在本文中,作为信息物理系统中重要的预处理步骤,提出了一种新的方法来抑制椒盐噪声,可以克服这一限制。该方法首先检测损坏的像素,然后使用通道方式的各向异性扩散来恢复它们。手段是双重的。一方面,边缘方法用于在每个通道中分别进行噪声抑制,因为污染像素分量是独立分布的。另一方面,将基于决策的各向异性扩散方法应用于每个通道以恢复它们。各向异性扩散是随时间的能量耗散过程,并且依赖于能量表面形状的几何分析。仿真结果表明,所提出的脉冲噪声去除方法达到了最先进的结果。
更新日期:2020-12-16
中文翻译:
彩色图像中椒盐噪声去除的基于决策的边际扩散
向量值图像的椒盐噪声抑制通常采用向量中值滤波、总变差 L1 模型、扩散方法和变体。然而,这些方法通常会引入过度平滑,并可能导致广泛的视觉特征模糊,并且仅适用于具有低强度噪声的图像。在本文中,作为信息物理系统中重要的预处理步骤,提出了一种新的方法来抑制椒盐噪声,可以克服这一限制。该方法首先检测损坏的像素,然后使用通道方式的各向异性扩散来恢复它们。手段是双重的。一方面,边缘方法用于在每个通道中分别进行噪声抑制,因为污染像素分量是独立分布的。另一方面,将基于决策的各向异性扩散方法应用于每个通道以恢复它们。各向异性扩散是随时间的能量耗散过程,并且依赖于能量表面形状的几何分析。仿真结果表明,所提出的脉冲噪声去除方法达到了最先进的结果。