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Efficient single image super-resolution via graph-constrained least squares regression

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Abstract

We explore in this paper an efficient algorithmic solution to single image super-resolution (SR). We propose the gCLSR, namely graph-Constrained Least Squares Regression, to super-resolve a high-resolution (HR) image from a single low-resolution (LR) observation. The basic idea of gCLSR is to learn a projection matrix mapping the LR image patch to the HR image patch space while preserving the intrinsic geometric structure of the original HR image patch manifold. Even if gCLSR resembles other manifold learning-based SR methods in preserving the local geometric structure of HR and LR image patch manifolds, the innovation of gCLSR lies in that it preserves the intrinsic geometric structure of the original HR image patch manifold rather than the LR image patch manifold, which may be contaminated by image degeneration (e.g., blurring, down-sampling and noise). Upon acquiring the projection matrix, the target HR image can be simply super-resolved from a single LR image without the need of HR-LR training pairs, which favors resource-limited applications. Experiments on images from the public database show that gCLSR method can achieve competitive quality as state-of-the-art methods, while gCLSR is much more efficient in computation than some state-of-the-art methods.

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Notes

  1. http://www.jdl.ac.cn/user/hchang

  2. http://www.ifp.illinois.edu/~jyang29

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Acknowledgment

The research was supported by the major national science and technology special projects (2010ZX03004-003-03, 2010ZX03004-001-03), the National Basic Research Program of China (973 Program) (2009CB320906), the National Natural Science Foundation of China (61231015, 61172173, 61003184, 61070080, 61170023).

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Authors and Affiliations

  1. National Engineering Research Center for Multimedia Software, School of Computer, Wuhan University, Wuhan, 430072, China

    Junjun Jiang, Ruimin Hu, Zhen Han & Tao Lu

  2. Faculty of Information, Wuhan University, Room 603, Academic Laboratory Building, Wuhan, 430072, People’s Republic of China

    Ruimin Hu

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  1. Junjun Jiang
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  2. Ruimin Hu
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  3. Zhen Han
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  4. Tao Lu
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Correspondence to Ruimin Hu.

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Jiang, J., Hu, R., Han, Z. et al. Efficient single image super-resolution via graph-constrained least squares regression. Multimed Tools Appl 72, 2573–2596 (2014). https://doi.org/10.1007/s11042-013-1567-9

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  • DOI: https://doi.org/10.1007/s11042-013-1567-9

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