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Image super-resolution via progressive cascading residual network
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Publication Year
2018-12-13
Journal
IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops
Publisher
IEEE Computer Society
Citation
IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, Vol.2018-June, pp.904-912
Mesh Keyword
Convolutional neural networkImage super resolutionsLearning approachLearning techniquesLow resolution imagesPerformance GainProgressive learningSuper resolution
All Science Classification Codes (ASJC)
Computer Vision and Pattern RecognitionElectrical and Electronic Engineering
Abstract
The problem of enhancing the resolution of a single low-resolution image has been popularly addressed by recent deep learning techniques. However, many deep learning approaches still fail to deal with extreme super-resolution scenarios because of the instability of training. In this paper, we address this issue by adapting a progressive learning scheme to the deep convolutional neural network. In detail, the overall training proceeds in multiple stages so that the model gradually increases the output image resolution. In our experiments, we show that this property yields a large performance gain compared to the non-progressive learning methods.
Language
eng
URI
https://aurora.ajou.ac.kr/handle/2018.oak/36270
https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85060897717&origin=inward
DOI
https://doi.org/10.1109/cvprw.2018.00123
Journal URL
http://ieeexplore.ieee.org/xpl/conferences.jsp
Type
Conference
Funding
This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education [NRF-2016R1D1A1B03933875].
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Sohn, Kyung-Ah Image
Sohn, Kyung-Ah손경아
Department of Software and Computer Engineering
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