Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
4969419 | Journal of Visual Communication and Image Representation | 2016 | 13 Pages |
Abstract
The easy-to-compute Anscombe transform offers a conversion of a Poisson random variable into a variance stabilized Gaussian one, thus becoming handy in various Poisson-noisy inverse problems. Solution to such problems can be done by applying this transform, then invoking a high-performance Gaussian-noise-oriented restoration algorithm, and finally using an inverse transform. This process works well for high-SNR images, but when the noise level is high, it loses much of its effectiveness. This work suggests a novel method for coupling Gaussian denoising algorithms to Poisson noisy inverse problems. This approach is based on a general approach termed “Plug-and-Play-Prior”. Deploying this to Poisson inverse-problems leads to an iterative scheme that repeats an easy treatable convex programming task, followed by a powerful Gaussian denoising This method, like the Anscombe transform, enables to plug Gaussian denoising algorithms for the Poisson-oriented problem, and yet, it is effective for all SNR ranges.
Keywords
Related Topics
Physical Sciences and Engineering
Computer Science
Computer Vision and Pattern Recognition
Authors
Arie Rond, Raja Giryes, Michael Elad,