Image Denoising based on Overlapped and Adaptive Gaussian Smoothing and Convolutional Refinement Networks

No data in Web of Science(Wrong one)

No data in Scopus (Wrong two)Server returned HTTP response code: 500 for URL: https://api.elsevier.com/content/search/scopus?apiKey=80c8c594bfc1533f4784bcba5a0cd6dd&query=DOI(10.1109%2FISM46123.2019.00032)+OR+TITLE-ABS-KEY%28%22Image+Denoising+based+on+Overlapped+and+Adaptive+Gaussian+Smoothing+and+Convolutional+Refinement+Networks%22%29

Title	Image Denoising based on Overlapped and Adaptive Gaussian Smoothing and Convolutional Refinement Networks
Creator	彭彥璁 Peng, Yan-Tsung Lin, M.-H. Tang, C.-L. Wu, C.-H.
Contributor	資科系
Date	2019-09
Date Issued	2-Mar-2020 15:23:01 (UTC+8)
Summary	We propose to use overlapped and adaptive Gaussian smoothing (OAGS) and convolutional refinement networks (CRN) to recover images corrupted by salt-and-pepper noise. First, the OAGS method identifies noise pixels and recover them. Then, CRN further improve and restore the recovered results with sharper and clearer edges. Experimental results demonstrate the proposed OAGS+CRN method significantly outperforms state-of-the-art denoising methods.
Relation	2019 IEEE International Symposium on Multimedia (ISM), University of California, Irvine
Type	conference
DOI	https://doi.org/10.1109/ISM46123.2019.00032

dc.contributor	資科系	-
dc.creator (作者)	彭彥璁	-
dc.creator (作者)	Peng, Yan-Tsung	-
dc.creator (作者)	Lin, M.-H.	-
dc.creator (作者)	Tang, C.-L.	-
dc.creator (作者)	Wu, C.-H.	-
dc.date (日期)	2019-09	-
dc.date.accessioned	2-Mar-2020 15:23:01 (UTC+8)	-
dc.date.available	2-Mar-2020 15:23:01 (UTC+8)	-
dc.date.issued (上傳時間)	2-Mar-2020 15:23:01 (UTC+8)	-
dc.identifier.uri (URI)	http://nccur.lib.nccu.edu.tw/handle/140.119/129021	-
dc.description.abstract (摘要)	We propose to use overlapped and adaptive Gaussian smoothing (OAGS) and convolutional refinement networks (CRN) to recover images corrupted by salt-and-pepper noise. First, the OAGS method identifies noise pixels and recover them. Then, CRN further improve and restore the recovered results with sharper and clearer edges. Experimental results demonstrate the proposed OAGS+CRN method significantly outperforms state-of-the-art denoising methods.	-
dc.format.extent	399342 bytes	-
dc.format.mimetype	application/pdf	-
dc.relation (關聯)	2019 IEEE International Symposium on Multimedia (ISM), University of California, Irvine	-
dc.title (題名)	Image Denoising based on Overlapped and Adaptive Gaussian Smoothing and Convolutional Refinement Networks	-
dc.type (資料類型)	conference	-
dc.identifier.doi (DOI)	10.1109/ISM46123.2019.00032	-
dc.doi.uri (DOI)	https://doi.org/10.1109/ISM46123.2019.00032	-