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Geometry Constrained Sparse Coding for Single Image Super-resolution
Xiaoqiang Lu; Haoliang Yuan; Pingkun Yan; Yuan Yuan; Xuelong Li
2012
会议名称IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
会议录名称2012 IEEE CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR)
页码1648-1655
会议日期JUN 16-21, 2012
会议地点Providence, RI
出版地NEW YORK
出版者IEEE
产权排序1
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.
作者部门光学影像分析与学习中心
收录类别CPCI(ISTP) ; EI
ISBN号978-1-4673-1228-8
语种英语
ISSN号1063-6919
文献类型会议论文
条目标识符http://ir.opt.ac.cn/handle/181661/20501
专题光学影像学习与分析中心
推荐引用方式
GB/T 7714
Xiaoqiang Lu,Haoliang Yuan,Pingkun Yan,et al. Geometry Constrained Sparse Coding for Single Image Super-resolution[C]. NEW YORK:IEEE,2012:1648-1655.
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