Publikace na Matematicko-fyzikální fakulta |
2016
Abstrakt
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.