Quasi-random nonlinear scale space
Pattern Recognition Letters
Generalized probabilistic scale space for image restoration
IEEE Transactions on Image Processing - Special section on distributed camera networks: sensing, processing, communication, and implementation
Iterative Parameter-Choice and Multigrid Methods for Anisotropic Diffusion Denoising
SIAM Journal on Scientific Computing
On the choice of the parameters for anisotropic diffusion in image processing
Pattern Recognition
Gradient-based Wiener filter for image denoising
Computers and Electrical Engineering
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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.