Cross-channel blur invariants of color and multispectral images

Abstract

The paper deals with the recognition of blurred color/multispectral images directly without any deblurring. We present a general theory of invariants of multispectral images with respect to blur. The paper is a significant non-trivial extension of the recent theory of blur invariants of graylevel images. The main original contribution of the paper lies in introducing cross-channel blur invariants in Fourier domain. We also developed an algorithm for their stable and fast calculation in the moment domain. Moreover, the cross-channel invariants can be found for blurs for which single-channel invariants do not exist. The experiments on simulated and real data demonstrate that incorporating the new cross-channel invariants significantly improves the recognition power and surpasses other existing approaches. The outlook for a possible implementation of the blur invariants into neural networks is briefly sketched in the conclusion.