SERF: A Simple, Effective, Robust, and Fast Image Super-Resolver From Cascaded Linear Regression | |
Hu, Yanting1; Wang, Nannan2; Tao, Dacheng3![]() ![]() | |
作者部门 | 光学影像学习与分析中心 |
2016-09-01 | |
发表期刊 | IEEE TRANSACTIONS ON IMAGE PROCESSING
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ISSN | 1057-7149 |
卷号 | 25期号:9页码:4091-4102 |
产权排序 | 4 |
摘要 | Example learning-based image super-resolution techniques estimate a high-resolution image from a low-resolution input image by relying on high-and low-resolution image pairs. An important issue for these techniques is how to model the relationship between high-and low-resolution image patches: most existing complex models either generalize hard to diverse natural images or require a lot of time for model training, while simple models have limited representation capability. In this paper, we propose a simple, effective, robust, and fast (SERF) image superresolver for image super-resolution. The proposed super-resolver is based on a series of linear least squares functions, namely, cascaded linear regression. It has few parameters to control the model and is thus able to robustly adapt to different image data sets and experimental settings. The linear least square functions lead to closed form solutions and therefore achieve computationally efficient implementations. To effectively decrease these gaps, we group image patches into clusters via k-means algorithm and learn a linear regressor for each cluster at each iteration. The cascaded learning process gradually decreases the gap of highfrequency detail between the estimated high-resolution image patch and the ground truth image patch and simultaneously obtains the linear regression parameters. Experimental results show that the proposed method achieves superior performance with lower time consumption than the state-of-the-art methods. |
文章类型 | Article |
关键词 | Cascaded Linear Regression Example Learning Based Image Super-resolution K-means |
WOS标题词 | Science & Technology ; Technology |
DOI | 10.1109/TIP.2016.2580942 |
收录类别 | SCI ; EI |
关键词[WOS] | SPARSE REPRESENTATION ; FACE ALIGNMENT ; SUPERRESOLUTION ; INTERPOLATION ; HALLUCINATION ; RESOLUTION |
语种 | 英语 |
WOS研究方向 | Computer Science ; Engineering |
项目资助者 | National Natural Science Foundation of China(61432014 ; Key Industrial Innovation Chain Project in Industrial Domain of Shaanxi Province ; Fundamental Research Funds for the Central Universities(BDZ021403 ; Microsoft Research Asia Project based Funding(FY13-RES-OPP-034) ; Program for Changjiang Scholars and Innovative Research Team in University of China(IRT13088) ; Shaanxi Innovative Research Team for Key Science and Technology(2012KCT-02) ; Australian Research Council(FT-130101457 ; China Post-Doctoral Science Foundation(2015M580818 ; 61501339) ; XJS15049 ; DP-140102164 ; 2016M590926 ; XJS15068 ; LE-140100061) ; 2016T90893) ; JB160104 ; JB160108 ; JB149901) |
WOS类目 | Computer Science, Artificial Intelligence ; Engineering, Electrical & Electronic |
WOS记录号 | WOS:000397743400001 |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://ir.opt.ac.cn/handle/181661/28248 |
专题 | 光谱成像技术研究室 |
作者单位 | 1.Xidian Univ, Sch Elect Engn, State Key Lab Integrated Serv Networks, Xian 710071, Peoples R China 2.Xidian Univ, Sch Telecommun Engn, State Key Lab Integrated Serv Networks, Xian 710071, Peoples R China 3.Univ Technol Sydney, Fac Engn & Informat Technol, Ctr Quantum Computat & Intelligent Syst, Ultimo, NSW 2007, Australia 4.Chinese Acad Sci, Xian Inst Opt & Precis Mech, State Key Lab Transient Opt & Photon, Ctr Opt Imagery Anal & Learning, Xian 710119, Peoples R China |
推荐引用方式 GB/T 7714 | Hu, Yanting,Wang, Nannan,Tao, Dacheng,et al. SERF: A Simple, Effective, Robust, and Fast Image Super-Resolver From Cascaded Linear Regression[J]. IEEE TRANSACTIONS ON IMAGE PROCESSING,2016,25(9):4091-4102. |
APA | Hu, Yanting,Wang, Nannan,Tao, Dacheng,Gao, Xinbo,&Li, Xuelong.(2016).SERF: A Simple, Effective, Robust, and Fast Image Super-Resolver From Cascaded Linear Regression.IEEE TRANSACTIONS ON IMAGE PROCESSING,25(9),4091-4102. |
MLA | Hu, Yanting,et al."SERF: A Simple, Effective, Robust, and Fast Image Super-Resolver From Cascaded Linear Regression".IEEE TRANSACTIONS ON IMAGE PROCESSING 25.9(2016):4091-4102. |
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SERF- A Simple, Effe(4639KB) | 期刊论文 | 作者接受稿 | 限制开放 | CC BY-NC-SA | 请求全文 |
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