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Regularization of multivalued images by means of a wavelet-based partial differential equation

机译:借助基于小波的偏微分方程对多值图像进行正则化

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摘要

In this work, a wavelet-based anisotropic diffusion partial differential equation (PDE) is developed. The new model makes use of a multiscale structure tensor as an extension of the single-scale structure tensor proposed by Di Zenzo. The multiscale structure tensor allows for accumulating multiscale gradient information of local regions. Thus, averaging properties are maintained while preserving edge structure. This structure tensor is used in an anisotropic diffusion process of multispectral images, namely, in the Perona-Malik model. Therefore, a more efficient and accurate formulation for edge-preserving diffusion is obtained.
机译:在这项工作中,开发了基于小波的各向异性扩散偏微分方程(PDE)。新模型利用多尺度结构张量作为Di Zenzo提出的单尺度结构张量的扩展。多尺度结构张量允许累积局部区域的多尺度梯度信息。因此,在保持边缘结构的同时,保持了平均特性。该结构张量用于多光谱图像的各向异性扩散过程中,即在Perona-Malik模型中。因此,获得了用于边缘保持扩散的更有效和准确的配方。

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