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  4. 光学影像学习与分析中心
Single Image Super-Resolution With Multiscale Similarity Learning

文献类型：期刊论文

作者Zhang, Kaibing1; Gao, Xinbo2; Tao, Dacheng3,4; Li, Xuelong5
刊名ieee transactions on neural networks and learning systems
出版日期2013-10-01
卷号24期号:10页码:1648-1659
关键词Image super-resolution (SR) multiscale self-similarities neighbor embedding (NE) nonlocal means (NLM)
英文摘要example learning-based image super-resolution (sr) is recognized as an effective way to produce a high-resolution (hr) image with the help of an external training set. the effectiveness of learning-based sr methods, however, depends highly upon the consistency between the supporting training set and low-resolution (lr) images to be handled. to reduce the adverse effect brought by incompatible high-frequency details in the training set, we propose a single image sr approach by learning multiscale self-similarities from an lr image itself. the proposed sr approach is based upon an observation that small patches in natural images tend to redundantly repeat themselves many times both within the same scale and across different scales. to synthesize the missing details, we establish the hr-lr patch pairs using the initial lr input and its down-sampled version to capture the similarities across different scales and utilize the neighbor embedding algorithm to estimate the relationship between the lr and hr image pairs. to fully exploit the similarities across various scales inside the input lr image, we accumulate the previous resultant images as training examples for the subsequent reconstruction processes and adopt a gradual magnification scheme to upscale the lr input to the desired size step by step. in addition, to preserve sharper edges and suppress aliasing artifacts, we further apply the nonlocal means method to learn the similarity within the same scale and formulate a nonlocal prior regularization term to well pose sr estimation under a reconstruction-based sr framework. experimental results demonstrate that the proposed method can produce compelling sr recovery both quantitatively and perceptually in comparison with other state-of-the-art baselines.
WOS标题词science & technology ; technology
类目[WOS]computer science, artificial intelligence ; computer science, hardware & architecture ; computer science, theory & methods ; engineering, electrical & electronic
研究领域[WOS]computer science ; engineering
关键词[WOS]quality assessment ; kernel regression ; nonlocal-means ; interpolation ; algorithm ; regularization ; restoration
收录类别SCI ; EI
语种英语
WOS记录号WOS:000325981400012
公开日期2015-06-30
源URL[http://ir.opt.ac.cn/handle/181661/23479]  
专题西安光学精密机械研究所_光学影像学习与分析中心
作者单位1.Hubei Engn Univ, Sch Comp & Informat Sci, Xiaogan 432000, Peoples R China
2.Xidian Univ, Sch Elect Engn, Xian 710071, Peoples R China
3.Univ Technol Sydney, Ctr Quantum Computat & Intelligent Syst, Ultimo, NSW 2007, Australia
4.Univ Technol Sydney, Fac Engn & Informat Technol, Ultimo, NSW 2007, Australia
5.Chinese Acad Sci, Xian Inst Opt & Precis Mech, Ctr Opt Imagery Anal & Learning, State Key Lab Transient Opt & Photon, Xian 710119, Peoples R China
推荐引用方式
GB/T 7714		 Zhang, Kaibing,Gao, Xinbo,Tao, Dacheng,et al. Single Image Super-Resolution With Multiscale Similarity Learning[J]. ieee transactions on neural networks and learning systems,2013,24(10):1648-1659.
APA Zhang, Kaibing,Gao, Xinbo,Tao, Dacheng,&Li, Xuelong.(2013).Single Image Super-Resolution With Multiscale Similarity Learning.ieee transactions on neural networks and learning systems,24(10),1648-1659.
MLA Zhang, Kaibing,et al."Single Image Super-Resolution With Multiscale Similarity Learning".ieee transactions on neural networks and learning systems 24.10(2013):1648-1659.

入库方式： OAI收割

来源：西安光学精密机械研究所

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