Abstract:
Image acquisition in many biomedical imaging modalities is corrupted by Poisson noise followed by additive Gaussian noise. MLE based restoration methods that use the exac...Show MoreMetadata
Abstract:
Image acquisition in many biomedical imaging modalities is corrupted by Poisson noise followed by additive Gaussian noise. MLE based restoration methods that use the exact Likelihood function for this mixed model with non-quadratic regularization are very few. While it has been demonstrated that total variation (TV) based regularization methods give better results, such methods that use exact Poisson-Gaussian Likelihood are slow. Here, we propose an ADMM based fast algorithm for image restoration using exact Poisson-Gaussian Likelihood function and TV regularization. Specifically, we propose a novel variable splitting approach that enables isolating the complexity in the exact MLE functional from the image blurring operation, allowing a fast Newton-like iteration on the MLE functional. This leads to a significantly improved convergence rate of the overall ADMM iteration. The effectiveness of the proposed method is demonstrated using restoration examples.
Date of Conference: 04-07 April 2018
Date Added to IEEE Xplore: 24 May 2018
ISBN Information:
Electronic ISSN: 1945-8452