Geometry Constrained Sparse Coding for Single Image Super-resolution

CORC > 西安光学精密机械研究所 > 中国科学院西安光学精密机械研究所 > 光学影像学习与分析中心

	Geometry Constrained Sparse Coding for Single Image Super-resolution
	Yuan Yuan
	2012
会议名称	ieee conference on computer vision and pattern recognition (cvpr)
会议日期	jun 16-21, 2012
会议地点	providence, ri
页码	1648-1655
英文摘要	the choice of the over-complete dictionary that sparsely represents data is of prime importance for sparse coding based image super-resolution. sparse coding is a typical unsupervised learning method to generate an over-complete dictionary. however, most of the sparse coding methods for image super-resolution fail to simultaneously consider the geometrical structure of the dictionary and corresponding coefficients, which may result in noticeable super-resolution reconstruction artifacts. in this paper, a novel sparse coding method is proposed to preserve the geometrical structure of the dictionary and the sparse coefficients of the data. moreover, the proposed method can preserve the incoherence of dictionary entries, which is critical for sparse representation. inspired by the development on non-local self-similarity and manifold learning, the proposed sparse coding method can provide the sparse coefficients and learned dictionary from a new perspective, which have both reconstruction and discrimination properties to enhance the learning performance. extensive experimental results on image super-resolution have demonstrated the effectiveness of the proposed method.
收录类别	CPCI（ISTP） ; EI
合作状况	国内
产权排序	1
会议录	2012 ieee conference on computer vision and pattern recognition (cvpr)
会议录出版者	ieee
会议录出版地	new york
语种	英语
ISSN号	1063-6919
ISBN号	978-1-4673-1228-8
内容类型	会议论文
源URL	[http://ir.opt.ac.cn/handle/181661/20501]
专题	西安光学精密机械研究所_光学影像学习与分析中心
推荐引用方式 GB/T 7714	Yuan Yuan. Geometry Constrained Sparse Coding for Single Image Super-resolution[C]. 见:ieee conference on computer vision and pattern recognition (cvpr). providence, ri. jun 16-21, 2012.