| Multi-task Gaussian Process Regression-based Image Super Resolution | |
| Jiang XW(蒋心为); Xinwei Jiang | |
| 2015-09 | |
| Conference Name | British Machine Vision Conference (BMVC) |
| Source Publication | The British Machine Vision Association |
| Conference Date | 2015.9.7-2015.9.10 |
| Conference Place | Swansea |
| Abstract | This 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. |
| Keyword | Super Resolution Multi-task Gaussian Process |
| Document Type | 会议论文 |
| Identifier | http://ir.ia.ac.cn/handle/173211/11964 |
| Collection | 综合信息系统研究中心 |
| Corresponding Author | Xinwei Jiang |
| Affiliation | Institute 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-SA | View Download | ||
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