Local phase quantization for blur-insensitive image analysis

Authors:
Esa Rahtu;Janne Heikkilä;Ville Ojansivu;Timo Ahonen
Affiliations:
Center for Machine Vision Research, University of Oulu, Finland;Center for Machine Vision Research, University of Oulu, Finland;Institute for Molecular Medicine Finland, University of Helsinki, Finland;Nokia Research Center, Palo Alto, CA, United States
Venue:
Image and Vision Computing
Year:
2012

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Abstract

One of the principal causes for image quality degradation is blur. This frequent phenomenon is usually a result of misfocused optics or camera motion, and it is very difficult to undo. Beyond the impaired visual quality, blurring causes problems to computer vision algorithms. In this paper, we present a simple yet powerful image descriptor, which is robust against the most common image blurs. The proposed method is based on quantizing the phase information of the local Fourier transform and it can be used to characterize the underlying image texture. We show how to construct several variants of our descriptor by varying the technique for local phase estimation and utilizing the proposed data decorrelation scheme. The descriptors are assessed in texture and face recognition experiments, and the results are compared with several state-of-the-art methods. The difference to the baseline is considerable in the case of blurred images, but also with sharp images our method gives a highly competitive performance.