Local phase quantization for blur-insensitive image analysis

Esa Rahtu

doi:10.1016/j.imavis.2012.04.001

Local phase quantization for blur-insensitive image analysis

2012-08-01

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AI

DOI：10.1016/j.imavis.2012.04.001

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Abstract

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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. (C) 2012 Elsevier B.V. All rights reserved.

Keywords:

Feature extraction

Invariant features

Blur invariance

Texture recognition

Face recognition

Local phase quantization for blur-insensitive image analysis

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