Single image super-resolution via subspace projection and neighbor embedding

	Single image super-resolution via subspace projection and neighbor embedding
	Xiaoyan Li; Hongjie He; Zhongke Yin; Fan Chen; Jun Cheng
刊名	NEUROCOMPUTING
	2014
英文摘要	In this paper, we present a novel learning-based single image super-resolution algorithm to address the problems of inefficient learning and improper estimation in coping with nonlinear high-dimensional feature data. Our method named as subspace projection and neighbor embedding (SPNE) first projects the high-dimensional data into two different subspaces respectively, i.e., kernel principal component analysis (KPCA) subspace and modified locality preserving projection (MLPP) subspace to obtain the global and local structures of data. In an optimal low-dimensional feature space, the k-nearest neighbors of each input low-resolution (LR) image patch can be found for efficient learning. Then within similarity measures and proportional factors, the k embedding weights are used to estimate high-frequency information from a training dataset. Finally, we apply iterative back projection (IBP) to further enhance the super-resolution results. Experiments on simulative and actual LR images demonstrate that the proposed approach outperforms the existing NE-based super-resolution methods in terms of visual quality and some selected objective metrics.
收录类别	SCI
原文出处	http://ac.els-cdn.com/S092523121400424X/1-s2.0-S092523121400424X-main.pdf?_tid=ca93fd7e-29d3-11e5-903a-00000aacb35d&acdnat=1436842711_c605d1fe821b43f30dfd4329cb7a6a90
语种	英语
内容类型	期刊论文
源URL	[http://ir.siat.ac.cn:8080/handle/172644/5414]
专题	深圳先进技术研究院_集成所
作者单位	NEUROCOMPUTING
推荐引用方式 GB/T 7714	Xiaoyan Li,Hongjie He,Zhongke Yin,et al. Single image super-resolution via subspace projection and neighbor embedding[J]. NEUROCOMPUTING,2014.
APA	Xiaoyan Li,Hongjie He,Zhongke Yin,Fan Chen,&Jun Cheng.(2014).Single image super-resolution via subspace projection and neighbor embedding.NEUROCOMPUTING.
MLA	Xiaoyan Li,et al."Single image super-resolution via subspace projection and neighbor embedding".NEUROCOMPUTING (2014).