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Adaptive Perona–Malik Model Based on the Variable Exponent for Image Denoising

机译:基于可变指数的Perona-Malik自适应模型用于图像去噪

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This paper introduces a class of adaptive Perona–Malik (PM) diffusion, which combines the PM equation with the heat equation. The PM equation provides a potential algorithm for image segmentation, noise removal, edge detection, and image enhancement. However, the defect of traditional PM model is tending to cause the staircase effect and create new features in the processed image. Utilizing the edge indicator as a variable exponent, we can adaptively control the diffusion mode, which alternates between PM diffusion and Gaussian smoothing in accordance with the image feature. Computer experiments indicate that the present algorithm is very efficient for edge detection and noise removal.
机译:本文介绍了一类自适应Perona–Malik(PM)扩散,它将PM方程与热方程结合在一起。 PM方程为图像分割,噪声去除,边缘检测和图像增强提供了一种潜在的算法。然而,传统PM模型的缺陷往往会导致阶梯效应,并在处理后的图像中产生新的特征。利用边缘指示器作为变量指数,我们可以自适应地控制扩散模式,该扩散模式根据图像特征在PM扩散和高斯平滑之间交替。计算机实验表明,本算法对于边缘检测和噪声去除非常有效。

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