• Laser & Optoelectronics Progress
  • Vol. 56, Issue 22, 221004 (2019)
Yakang Duan, Lin Luo, Jinlong Li*, and Xiaorong Gao
Author Affiliations
  • School of Physical Science and Technology, Southwest Jiaotong University, Chengdu, Sichuan 610031, China
  • show less
    DOI: 10.3788/LOP56.221004 Cite this Article Set citation alerts
    Yakang Duan, Lin Luo, Jinlong Li, Xiaorong Gao. Super-Resolution Reconstruction of Astronomical Images Based on Centralized Sparse Representation[J]. Laser & Optoelectronics Progress, 2019, 56(22): 221004 Copy Citation Text show less
    References

    [1] Hirsch M, Harmeling S, Sra S et al. Online multi-frame blind deconvolution with super-resolution and saturation correction[J]. Astronomy & Astrophysics, 531, A9(2011). http://www.jstor.org/servlet/linkout?suffix=rf25&dbid=16&doi=10.1086%2F673168&key=10.1051%2F0004-6361%2F200913955

    [2] Liu Q S, Bai J, Yu F H. Astronomical image denoising by means of improved adaptive backtracking-based matching pursuit algorithm[J]. Applied Optics, 53, 7796-7803(2014). http://www.opticsinfobase.org/abstract.cfm?uri=ao-53-32-7796

    [3] Li Z, Peng Q Y, Bhanu B et al. Super resolution for astronomical observations[J]. Astrophysics and Space Science, 363, 92(2018). http://link.springer.com/article/10.1007/s10509-018-3315-0

    [4] Gong R, Wang Y, Cai Y L et al. How to deal with color in super resolution reconstruction of images[J]. Optics Express, 25, 11144-11156(2017). http://www.onacademic.com/detail/journal_1000040493359610_e1e2.html

    [5] Li M, Nguyen T Q. Markov random field model-based edge-directed image interpolation[J]. IEEE Transactions on Image Processing, 17, 1121-1128(2008). http://www.ncbi.nlm.nih.gov/pubmed/18586620

    [6] Shi W J. TGV regularized super resolution reconstruction for infrared remote sensing image[J]. Laser & Optoelectronics Progress, 55, 091004(2018).

    [7] Liu J Y, Yang W H, Zhang X F et al. Retrieval compensated group structured sparsity for image super-resolution[J]. IEEE Transactions on Multimedia, 19, 302-316(2017). http://ieeexplore.ieee.org/document/7579175/

    [8] Thapa D, Raahemifar K, Bobier W R et al. A performance comparison among different super-resolution techniques[J]. Computers & Electrical Engineering, 54, 313-329(2016). http://dl.acm.org/citation.cfm?id=3010271

    [9] Li L, Xie Y, Hu W R et al. Single image super-resolution using combined total variation regularization by split Bregman iteration[J]. Neurocomputing, 142, 551-560(2014). http://dl.acm.org/citation.cfm?id=2657119

    [10] Qiu K, Yi B S, Xiang M et al. Collaborative sparse dictionary learning for reconstruction of single image super resolution[J]. Acta Optica Sinica, 38, 0910002(2018).

    [11] Chang H, Yeung D Y, Xiong Y M. Super-resolution through neighbor embedding. [C]//Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2004. CVPR 2004., June 27-July 2, 2004, Washington, DC, USA. New York: IEEE, 8152809(2004).

    [12] Yang J C, Wright J, Huang T et al. Image super-resolution as sparse representation of raw image patches. [C]//2008 IEEE Conference on Computer Vision and Pattern Recognition, June 23-28, 2008, Anchorage, AK, USA. New York: IEEE, 10139952(2008).

    [13] Yang J C, Wright J, Huang T S et al. Image super-resolution via sparse representation[J]. IEEE Transactions on Image Processing, 19, 2861-2873(2010).

    [14] Zeyde R, Elad M, Protter M. On single image scale-up using sparse-representations[M]. //Boissonnat J D, Chenin P, Cohen A, et al. Curves and surfaces. Lecture notes in computer science. Berlin, Heidelberg: Springer, 6920, 711-730(2012).

    [15] Timofte R, De V, Gool L V. Anchored neighborhood regression for fast example-based super-resolution. [C]//2013 IEEE International Conference on Computer Vision, December 1-8, 2013, Sydney, NSW, Australia. New York: IEEE, 1920-1927(2013).

    [16] Dong W S, Zhang L, Shi G M et al. Image deblurring and super-resolution by adaptive sparse domain selection and adaptive regularization[J]. IEEE Transactions on Image Processing, 20, 1838-1857(2011). http://www.ncbi.nlm.nih.gov/pubmed/21278019/

    [17] Dong W S, Zhang L, Shi G M et al. Nonlocally centralized sparse representation for image restoration[J]. IEEE Transactions on Image Processing, 22, 1620-1630(2013). http://europepmc.org/abstract/med/23269751

    [18] Chu J H, Hu F S, Zhang J Q et al. An improved single-frame super-resolution algorithm for magnetic resonance image[J]. Laser & Optoelectronics Progress, 55, 051009(2018).

    [19] Elad M, Aharon M. Image denoising via sparse and redundant representations over learned dictionaries[J]. IEEE Transactions on Image Processing, 15, 3736-3745(2006). http://ieeexplore.ieee.org/document/4011956/

    [20] Glasner D, Bagon S, Irani M. Super-resolution from a single image. [C]//2009 IEEE 12th International Conference on Computer Vision, September 29-October 2, 2009, Kyoto, Japan. New York: IEEE, 349-356(2009).

    [21] Yang S Y, Liu Z Z, Wang M et al. Multitask dictionary learning and sparse representation based single-image super-resolution reconstruction[J]. Neurocomputing, 74, 3193-3203(2011). http://www.sciencedirect.com/science/article/pii/S0925231211002839

    [22] Xu R. WunschII D. Survey of clustering algorithms[J]. IEEE Transactions on Neural Networks, 16, 645-678(2005).

    [23] Aharon M, Elad M, Bruckstein A. K-SVD: an algorithm for designing overcomplete dictionaries for sparse representation[J]. IEEE Transactions on Signal Processing, 54, 4311-4322(2006). http://bioinformatics.oxfordjournals.org/external-ref?access_num=10.1109/TSP.2006.881199&link_type=DOI

    [24] Daubechies I. Defrise M, de Mol C. An iterative thresholding algorithm for linear inverse problems with a sparsity constraint[J]. Communications on Pure and Applied Mathematics, 57, 1413-1457(2004). http://onlinelibrary.wiley.com/doi/10.1002/cpa.20042/abstract

    Yakang Duan, Lin Luo, Jinlong Li, Xiaorong Gao. Super-Resolution Reconstruction of Astronomical Images Based on Centralized Sparse Representation[J]. Laser & Optoelectronics Progress, 2019, 56(22): 221004
    Download Citation