DC Field | Value | Language |
---|---|---|
dc.contributor.author | Yoon, Youngjin | ko |
dc.contributor.author | Jeon, Hae-Gon | ko |
dc.contributor.author | 이준영 | ko |
dc.contributor.author | 유동근 | ko |
dc.contributor.author | Kweon, In-So | ko |
dc.date.accessioned | 2016-04-18T04:41:45Z | - |
dc.date.available | 2016-04-18T04:41:45Z | - |
dc.date.created | 2015-11-24 | - |
dc.date.created | 2015-11-24 | - |
dc.date.created | 2015-11-24 | - |
dc.date.issued | 2015-12-11 | - |
dc.identifier.citation | IEEE International Conference on Computer Vision (ICCV 2015), pp.57 - 65 | - |
dc.identifier.uri | http://hdl.handle.net/10203/204139 | - |
dc.description.abstract | Commercial Light-Field cameras provide spatial and angular information, but its limited resolution becomes an important problem in practical use. In this paper, we present a novel method for Light-Field image super-resolution (SR) via a deep convolutional neural network. Rather than the conventional optimization framework, we adopt a datadriven learning method to simultaneously up-sample the angular resolution as well as the spatial resolution of a Light-Field image. We first augment the spatial resolution of each sub-aperture image to enhance details by a spatial SR network. Then, novel views between the sub-aperture images are generated by an angular super-resolution network. These networks are trained independently but finally finetuned via end-to-end training. The proposed method shows the state-of-the-art performance on HCI synthetic dataset, and is further evaluated by challenging real-world applications including refocusing and depth map estimation | - |
dc.language | English | - |
dc.publisher | IEEE Computer Society and the Computer Vision Foundation (CVF) | - |
dc.title | Learning a Deep Convolutional Network for Light-Field Image Super-Resolution | - |
dc.type | Conference | - |
dc.identifier.wosid | 000380434700008 | - |
dc.identifier.scopusid | 2-s2.0-84962026650 | - |
dc.type.rims | CONF | - |
dc.citation.beginningpage | 57 | - |
dc.citation.endingpage | 65 | - |
dc.citation.publicationname | IEEE International Conference on Computer Vision (ICCV 2015) | - |
dc.identifier.conferencecountry | CL | - |
dc.identifier.conferencelocation | Santiago, Chile | - |
dc.identifier.doi | 10.1109/ICCVW.2015.17 | - |
dc.embargo.liftdate | 9999-12-31 | - |
dc.embargo.terms | 9999-12-31 | - |
dc.contributor.localauthor | Kweon, In-So | - |
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