Implementation for noise suppression in images

摘要

Random noise in images represents the primary problem for early visual processing. This paper describes an adaptive surface labelling technique (ASL) that suppresses image noise by using data-driven rules that concern surface continuity. The algorithm first obtains a global estimate of the noise distribution and then tries to fit a surface to each part of the image. If this can be done so that the error in fitting a surface is within what would be expected from known noise statistics, then the central pixel is reset to lie on that surface. Analytical results are presented which demonstrate algorithm success provided that the number of samples in the patch is greater than 2m, where m is the number of linear parameters that determine the form of the patch.