Notes on performance of bidiagonalization-based estimator in image deblurring

• ID: 2729, RIV: 10331357
• ISSN: not specified, ISBN: 978-80-227-4544-4
• source: Proceedings of the Conference Algoritmy 2016
• keywords: image deblurring; linear ill-posed problem; noise; noise level estimate; Golub-Kahan iterative bidiagonalization
• authors: Iveta Hnětynková, Marie Kubínová, Martin Plešinger
• authors from KNM: Hnětynková Iveta

Abstract

Image deblurring represents one of important areas of image processing. When information about the amount of noise in the given blurred image is available, it can significantly improve the performance of image deblurring algorithms. The paper [11] introduced an iterative method for estimating the noise level in linear algebraic ill-posed problems contaminated by white noise. Here we study applicability of this approach to image deblurring problems with various types of blurring operators. White as well as data-correlated noise of various sizes is considered.