Classifying Discriminative Features for Blur Detection
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.
IEEE Transactions on Cybernetics