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Image segmentation and edge enhancement with stabilized inverse diffusion equations

机译:利用稳定的逆扩散方程进行图像分割和边缘增强

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We introduce a family of first-order multidimensional ordinary differential equations (ODEs) with discontinuous right-hand sides and demonstrate their applicability in image processing. An equation belonging to this family is an inverse diffusion everywhere except at local extrema, where some stabilization is introduced. For this reason, we call these equations "stabilized inverse diffusion equations" (SIDEs). Existence and uniqueness of solutions, as well as stability, are proven for SIDEs. A SIDE in one spatial dimension may be interpreted as a limiting case of a semi-discretized Perona-Malik equation (1990, 19994). In an experiment, SIDE's are shown to suppress noise while sharpening edges present in the input signal. Their application to image segmentation is also demonstrated.
机译:我们介绍了具有不连续右手边的一阶多维常微分方程(ODE),并证明了它们在图像处理中的适用性。属于这个族的一个方程是除局部极值以外的各处的逆扩散,在局部极值中引入了一些稳定作用。因此,我们称这些方程为“稳定逆扩散方程”(SIDE)。解决方案的存在性和唯一性以及稳定性已为SIDE证明。一个空间维度上的SIDE可以解释为半离散Perona-Malik方程的极限情况(1990,19994)。在实验中,显示了SIDE抑制噪声,同时锐化了输入信号中存在的边缘。还演示了它们在图像分割中的应用。

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