CASIA OpenIR  > 综合信息系统研究中心
Multi-task Gaussian Process Regression-based Image Super Resolution
Jiang XW(蒋心为); Xinwei Jiang
Conference NameBritish Machine Vision Conference (BMVC)
Source PublicationThe British Machine Vision Association
Conference Date2015.9.7-2015.9.10
Conference PlaceSwansea
AbstractThis paper presents a novel framework for image super resolution (SR) based on the multi-task gaussian process (MTGP) regression. The core idea is to treat each pixel prediction using gaussian process regression as one single task and cast recovering a high resolution image patch as a multi-task learning problem. In contrast to prior Gaussian process regression-based SR approaches, our algorithm induces the inter-task correlation for considering image structures. We demonstrate the efficiency and effectiveness of the proposed method by applying it to the classic image dataset and experimental results show our approach is competitive with even outperforms the related and state-of-the-art methods.
KeywordSuper Resolution Multi-task Gaussian Process
Document Type会议论文
Corresponding AuthorXinwei Jiang
AffiliationInstitute of Automation,Chinese Academy of Sciences
Recommended Citation
GB/T 7714
Jiang XW,Xinwei Jiang. Multi-task Gaussian Process Regression-based Image Super Resolution[C],2015.
Files in This Item: Download All
File Name/Size DocType Version Access License
504 bmvc_final.pdf(1193KB)会议论文 开放获取CC BY-NC-SAView Download
Related Services
Recommend this item
Usage statistics
Export to Endnote
Google Scholar
Similar articles in Google Scholar
[Jiang XW(蒋心为)]'s Articles
[Xinwei Jiang]'s Articles
Baidu academic
Similar articles in Baidu academic
[Jiang XW(蒋心为)]'s Articles
[Xinwei Jiang]'s Articles
Bing Scholar
Similar articles in Bing Scholar
[Jiang XW(蒋心为)]'s Articles
[Xinwei Jiang]'s Articles
Terms of Use
No data!
Social Bookmark/Share
File name: 504 bmvc_final.pdf
Format: Adobe PDF
All comments (0)
No comment.

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