Show simple item record

dc.contributor.authorKim, Beomseoken_US
dc.contributor.authorSon, Hyeongseoken_US
dc.contributor.authorPark, Seong-Jinen_US
dc.contributor.authorCho, Sunghyunen_US
dc.contributor.authorLee, Seungyongen_US
dc.contributor.editorFu, Hongbo and Ghosh, Abhijeet and Kopf, Johannesen_US
dc.date.accessioned2018-10-07T14:59:44Z
dc.date.available2018-10-07T14:59:44Z
dc.date.issued2018
dc.identifier.issn1467-8659
dc.identifier.urihttps://doi.org/10.1111/cgf.13567
dc.identifier.urihttps://diglib.eg.org:443/handle/10.1111/cgf13567
dc.description.abstractWe propose a novel approach for detecting two kinds of partial blur, defocus and motion blur, by training a deep convolutional neural network. Existing blur detection methods concentrate on designing low-level features, but those features have difficulty in detecting blur in homogeneous regions without enough textures or edges. To handle such regions, we propose a deep encoder-decoder network with long residual skip-connections and multi-scale reconstruction loss functions to exploit high-level contextual features as well as low-level structural features. Another difficulty in partial blur detection is that there are no available datasets with images having both defocus and motion blur together, as most existing approaches concentrate only on either defocus or motion blur. To resolve this issue, we construct a synthetic dataset that consists of complex scenes with both types of blur. Experimental results show that our approach effectively detects and classifies blur, outperforming other state-of-the-art methods. Our method can be used for various applications, such as photo editing, blur magnification, and deblurring.en_US
dc.publisherThe Eurographics Association and John Wiley & Sons Ltd.en_US
dc.subjectComputing methodologies
dc.subjectImage processing
dc.titleDefocus and Motion Blur Detection with Deep Contextual Featuresen_US
dc.description.seriesinformationComputer Graphics Forum
dc.description.sectionheadersTowards Better Quality of Images/Videos
dc.description.volume37
dc.description.number7
dc.identifier.doi10.1111/cgf.13567
dc.identifier.pages277-288


Files in this item

Thumbnail
Thumbnail

This item appears in the following Collection(s)

  • 37-Issue 7
    Pacific Graphics 2018 - Symposium Proceedings

Show simple item record