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Classifying Discriminative Features for Blur Detection.
- Source :
- IEEE Transactions on Cybernetics; Oct2016, Vol. 46 Issue 10, p2220-2227, 8p
- Publication Year :
- 2016
-
Abstract
- Blur detection in a single image is challenging especially when the blur is spatially-varying. Developing discriminative blur features is an open problem. In this paper, we propose a new kernel-specific feature vector consisting of the information of a blur kernel and the information of an image patch. Specifically, the kernel specific-feature is composed of the multiplication of the variance of filtered kernel and the variance of filtered patch gradients. The feature origins from a blur-classification theorem and its discrimination can also be intuitively explained. To make the kernel-specific features useful for real applications, we build a pool of kernels consisting of motion-blur kernels, defocus-blur (out-of-focus) kernels, and their combinations. By extracting such features followed by the classifiers, the proposed algorithm outperforms the state-of-the-art blur detection method. Experimental results on public databases demonstrate the effectiveness of the proposed method. [ABSTRACT FROM PUBLISHER]
Details
- Language :
- English
- ISSN :
- 21682267
- Volume :
- 46
- Issue :
- 10
- Database :
- Complementary Index
- Journal :
- IEEE Transactions on Cybernetics
- Publication Type :
- Academic Journal
- Accession number :
- 118110083
- Full Text :
- https://doi.org/10.1109/TCYB.2015.2472478