Discriminative Blur Detection Features
Refereed conference paper presented and published in conference proceedings

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AbstractUbiquitous image blur brings out a practically important 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. Unlike previous methods, which are often based on restoration mechanisms, our features are constructed to enhance discriminative power and are adaptive to various blur scales in images. To avail evaluation, we build a new blur perception dataset containing thousands of images with labeled ground-truth. Our results are applied to several applications, including blur region segmentation, deblurring, and blur magnification.
All Author(s) ListShi JP, Xu L, Jia JY
Name of Conference27th IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
Start Date of Conference23/06/2014
End Date of Conference28/06/2014
Place of ConferenceColumbus
Country/Region of ConferenceUnited States of America
Detailed descriptionIEEE
Pages2965 - 2972
LanguagesEnglish-United Kingdom
Web of Science Subject CategoriesComputer Science; Computer Science, Artificial Intelligence

Last updated on 2021-16-06 at 01:17