PSF accuracy measure for evaluation of blur estimation algorithms
Jan Kotera, Barbara Zitová, Filip Šroubek · 2015
Given the large amount of blur estimation and blind deconvolution methods just in the last decade, there is an increasing need to compare the performance of a particular method with others. Unlike in other fields in image processing, there are very few well-established benchmark databases of test data and, more importantly, no standard way of performance evaluation. In this paper, we focus on the latter. We propose a new error measure for the blur kernel - a method for comparison of the blur estimate with the ground truth - which correctly reflects how inaccuracies in the blur estimation affect the subsequent image restoration, without the necessity to perform the actual deconvolution.