AbstractAnisotropic partial differential equations (PDEs) based schemes for denoising digital images are fast becoming an indispens'/> Image denoising by anisotropic diffusion with inter-scale information fusion
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Image denoising by anisotropic diffusion with inter-scale information fusion

机译:通过与级别信息融合的各向异性扩散的图像去噪

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AbstractAnisotropic partial differential equations (PDEs) based schemes for denoising digital images are fast becoming an indispensable tool in computer vision problems. In this paper we propose to denoise noisy images via such multiscale anisotropic diffusion. In general, digital images contain objects of multiple scales and denoising them without destroying edges is one of the main objective in early computer vision problems. Unlike the previous approaches, which discard the multiple scale based images produced by anisotropic PDE, we utilize information contained in them. By effectively combining the inter-scale details, the proposed scheme improves upon the noise removal and detail preservation properties over other schemes. Numerical results indicate that the scheme achieves good denoising with edge preservation on a variety of images.]]>
机译:Abstract各向异性偏微分方程(PDE)基于小波变换的数字图像去噪方法正迅速成为计算机视觉问题中不可或缺的工具。在本文中,我们建议通过这种多尺度各向异性扩散去噪图像。一般来说,数字图像包含多个尺度的目标,在不破坏边缘的情况下对其进行去噪是早期计算机视觉问题的主要目标之一。与之前的方法不同,我们利用了各向异性偏微分方程生成的多尺度图像中包含的信息。通过有效地结合尺度间细节,与其他方案相比,该方案提高了噪声去除和细节保持性能。数值结果表明,该方法在多种图像上均能实现良好的去噪和边缘保持]>

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