Nonlocal Evolutions for Image Regularization

摘要

A nonlocal quadratic functional of weighted differences is examined. The weights are based on image features and represent the affinity between different pixels in the image. By prescribing different formulas for the weights, one can generalize many local and nonlocal linear denoising algorithms, including nonlocal means and bilateral niters. The steepest descent for minimizing the functional can be interpreted as a nonlocal diffusion process. We show state of the art denoising results using the nonlocal flow.