Issue No. 12 - December (2008 vol. 30)
DOI Bookmark: http://doi.ieeecomputersociety.org/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.
Smoothing, Parabolic equations, Partial Differential Equations
G. Gilboa, "Nonlinear Scale Space with Spatially Varying Stopping Time," in IEEE Transactions on Pattern Analysis & Machine Intelligence, vol. 30, no. , pp. 2175-2187, 2008.