Discriminative Blur Detection Features

Ubiquitous image blur brings out a practically impor-tant question – what are effective features to differentiate between blurred and unblurred image regions. We address it by studying a few blur feature representations in image gradient, Fourier domain, and data-driven local filters. Un-like previous methods, which are often based on restoration mechanisms, our features are constructed to enhance dis-criminative power and are adaptive to various blur scales in images. To avail evaluation, we build a new blur percep-tion dataset containing thousands of images with labeled ground-truth. Our results are applied to several applica-tions, including blur region segmentation, deblurring, and blur magnification. 1