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Geometry Constrained Sparse Coding for Single Image Super-resolution
Xiaoqiang Lu; Haoliang Yuan; Pingkun Yan; Yuan Yuan; Xuelong Li
2012
Conference NameIEEE Conference on Computer Vision and Pattern Recognition (CVPR)
Source Publication2012 IEEE CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR)
Pages1648-1655
Conference DateJUN 16-21, 2012
Conference PlaceProvidence, RI
Publication PlaceNEW YORK
PublisherIEEE
Contribution Rank1
AbstractThe 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.
Department光学影像分析与学习中心
Indexed ByCPCI(ISTP) ; EI
ISBN978-1-4673-1228-8
Language英语
ISSN1063-6919
Document Type会议论文
Identifierhttp://ir.opt.ac.cn/handle/181661/20501
Collection光学影像学习与分析中心
Recommended Citation
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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