Monotonically decreasing eigenvalue for edge-sharpening diffusion

Wenhua Ma
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引用次数: 1

Abstract

Anisotropic diffusion is classified by the eigenvalue of the Hessian matrix associated with the diffusivity function into two categories: one incapable of edge-sharpening and the other capable of selective edge sharpening. A third class is proposed: the eigenvalue starts with a small value and decreases monotonically with image gradient magnitude so that the stronger the edge is, the more it is sharpened. Two such examples are given and one is found to consistently produce the best PSNR at all simulated noise levels.
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边缘锐化扩散的单调递减特征值
根据与扩散函数相关联的Hessian矩阵的特征值,将各向异性扩散分为不能锐化边缘和能选择性锐化边缘两类。提出了第三类特征值:特征值从一个较小的值开始,随着图像梯度幅度的增大而单调减小,使边缘越强越锐化。给出了两个这样的例子,其中一个例子在所有模拟噪声水平下都能产生最佳的PSNR。
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