Gilboa Guy
3DB Systems Ltd, Yokneam, Israel.
IEEE Trans Pattern Anal Mach Intell. 2008 Dec;30(12):2175-87. doi: 10.1109/TPAMI.2008.23.
A general scale space algorithm is presented for denoising signals and images with spatially varying dominant scales. The process is formulated as a partial differential equation with spatially varying time. The proposed adaptivity is semi-local and is in conjunction with the classical gradient-based diffusion coefficient, designed to preserve edges. The new algorithm aims at maximizing a local SNR measure of the denoised image. It is based on a generalization of a global stopping time criterion presented recently by the author and colleagues. Most notably, the method works well also for partially textured images and outperforms any selection of a global stopping time. Given an estimate of the noise variance, the procedure is automatic and can be applied well to most natural images.
提出了一种通用的尺度空间算法，用于对具有空间变化主导尺度的信号和图像进行去噪。该过程被表述为一个具有空间变化时间的偏微分方程。所提出的自适应是半局部的，并且与经典的基于梯度的扩散系数相结合，旨在保留边缘。新算法旨在使去噪图像的局部信噪比测量最大化。它基于作者及其同事最近提出的全局停止时间准则的推广。最值得注意的是，该方法对于部分纹理图像也能很好地工作，并且优于任何全局停止时间的选择。给定噪声方差的估计值，该过程是自动的，并且可以很好地应用于大多数自然图像。
