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Multi-task Gaussian Process Regression-based Image Super Resolution
Jiang XW(蒋心为); Xinwei Jiang
2015-09
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
Identifierhttp://ir.ia.ac.cn/handle/173211/11964
Collection综合信息系统研究中心
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.
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