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Image distortion detection using convolutional neural networkoa mark
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Publication Year
2018-12-13
Journal
Proceedings - 4th Asian Conference on Pattern Recognition, ACPR 2017
Publisher
Institute of Electrical and Electronics Engineers Inc.
Citation
Proceedings - 4th Asian Conference on Pattern Recognition, ACPR 2017, pp.226-231
Mesh Keyword
Convolutional neural networkDistortion regionsImage distortionsLocal compressionState of the art
All Science Classification Codes (ASJC)
Artificial IntelligenceComputer Vision and Pattern RecognitionSignal Processing
Abstract
Image distortion classification and detection is an im-portant task in many applications. For example when com-pressing images, if we know the exact location of the distortion, then it is possible to re-compress images by adjusting the local compression level dynamically. In this paper, we address the problem of detecting the distortion region and classifying the distortion type of a given image. We show that our model significantly outperforms the state-of-The-Art distortion classifier, and report accurate detection results for the first time. We expect that such results prove the use-fulness of our approach in many potential applications such as image compression or distortion restoration.
Language
eng
URI
https://aurora.ajou.ac.kr/handle/2018.oak/36260
https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85060545393&origin=inward
DOI
https://doi.org/10.1109/acpr.2017.95
Journal URL
http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=8575041
Type
Conference
Funding
N.Ahn and K.-A. Sohn were supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education [NRF-2016R1D1A1B03933875], and B.Kang by [NRF-2016R1A6A3A11932796].
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Sohn, Kyung-Ah Image
Sohn, Kyung-Ah손경아
Department of Software and Computer Engineering
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