CASIA OpenIR  > 模式识别国家重点实验室  > 先进数据分析与学习
Edge-directed single image super-resolution via cross-resolution sharpening function learning
Han, Wei1,2; Chu, Jun1,2; Wang, Lingfeng3; Pan, Chunhong3
Source PublicationMULTIMEDIA TOOLS AND APPLICATIONS
2017-04-01
Volume76Issue:8Pages:11143-11155
SubtypeArticle
AbstractEdge-directed single image super-resolution methods have been paid more attentions due to their sharp edge preserving in the recovered high-resolution image. Their core is the high-resolution gradient estimation. In this paper, we propose a novel cross-resolution gradient sharpening function learning to obtain the high-resolution gradient. The main idea of cross-resolution learning is to learn a sharpening function from low-resolution, and use it in high-resolution. Specifically, a blurred low-resolution image is first constructed by performing bicubic down-sampling and up-sampling operations sequentially. The gradient sharpening function considered as a linear transform is learned from blurred low-resolution gradient to the input low-resolution image gradient. After that, the high-resolution gradient is estimated by applying the learned gradient sharpening function to the initial blurred gradient obtained from the bicubic up-sampled of the low-resolution image. Finally, edge-directed single image super-resolution reconstruction is performed to obtain the sharpened high-resolution image. Extensive experiments demonstrate the effectiveness of our method in comparison with the state-of-the-art approaches.
KeywordSuper-resolution Gradient Magnitude Transformation Linear Transformation Function
WOS HeadingsScience & Technology ; Technology
DOI10.1007/s11042-016-3656-z
WOS KeywordRECONSTRUCTION ; LIMITS
Indexed BySCI
Language英语
Funding OrganizationNational Natural Science Foundation of China(61263046 ; 61403376 ; 61175025)
WOS Research AreaComputer Science ; Engineering
WOS SubjectComputer Science, Information Systems ; Computer Science, Software Engineering ; Computer Science, Theory & Methods ; Engineering, Electrical & Electronic
WOS IDWOS:000400570400048
Citation statistics
Cited Times:2[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/15262
Collection模式识别国家重点实验室_先进数据分析与学习
Affiliation1.Nanchang Hangkong Univ, Inst Comp Vis, Nanchang, Jiangxi, Peoples R China
2.Nanchang Hangkong Univ, Key Laborator Jiangxi Prov Image Proc & Pattern R, Nanchang, Jiangxi, Peoples R China
3.Chinese Acad Sci, Inst Automat, NLPR, Beijing, Peoples R China
Recommended Citation
GB/T 7714
Han, Wei,Chu, Jun,Wang, Lingfeng,et al. Edge-directed single image super-resolution via cross-resolution sharpening function learning[J]. MULTIMEDIA TOOLS AND APPLICATIONS,2017,76(8):11143-11155.
APA Han, Wei,Chu, Jun,Wang, Lingfeng,&Pan, Chunhong.(2017).Edge-directed single image super-resolution via cross-resolution sharpening function learning.MULTIMEDIA TOOLS AND APPLICATIONS,76(8),11143-11155.
MLA Han, Wei,et al."Edge-directed single image super-resolution via cross-resolution sharpening function learning".MULTIMEDIA TOOLS AND APPLICATIONS 76.8(2017):11143-11155.
Files in This Item: Download All
File Name/Size DocType Version Access License
1-5.pdf(5043KB)期刊论文作者接受稿开放获取CC BY-NC-SAView Download
Related Services
Recommend this item
Bookmark
Usage statistics
Export to Endnote
Google Scholar
Similar articles in Google Scholar
[Han, Wei]'s Articles
[Chu, Jun]'s Articles
[Wang, Lingfeng]'s Articles
Baidu academic
Similar articles in Baidu academic
[Han, Wei]'s Articles
[Chu, Jun]'s Articles
[Wang, Lingfeng]'s Articles
Bing Scholar
Similar articles in Bing Scholar
[Han, Wei]'s Articles
[Chu, Jun]'s Articles
[Wang, Lingfeng]'s Articles
Terms of Use
No data!
Social Bookmark/Share
File name: 1-5.pdf
Format: Adobe PDF
All comments (0)
No comment.
 

Items in the repository are protected by copyright, with all rights reserved, unless otherwise indicated.