• Laser & Optoelectronics Progress
  • Vol. 56, Issue 4, 041001 (2019)
Song Xue1, Siyu Zhang2、**, and Yongfeng Liu1、*
Author Affiliations
  • 1 Department of Weapons Engineering, Army Academy of Artillery and Air Defense, Hefei, Anhui 230000, China
  • 2 Postgraduate Team 1, Army Academy of Artillery and Air Defense, Hefei, Anhui 230000, China
  • show less
    DOI: 10.3788/LOP56.041001 Cite this Article Set citation alerts
    Song Xue, Siyu Zhang, Yongfeng Liu. Quality Assessment of Hyperspectral Super-Resolution Images[J]. Laser & Optoelectronics Progress, 2019, 56(4): 041001 Copy Citation Text show less
    References

    [1] Huang H, He K, Zheng X L et al. Spatial-spectral feature extraction of hyperspectral image based on deep learning[J]. Laser & Optoelectronics Progress, 54, 101001(2017).

    [2] Xu M E, Xie B L, Xu G M. Hyperspectral image super-resolution method based on spatial spectral joint sparse representation[J]. Laser & Optoelectronics Progress, 55, 071014(2018).

    [3] Yang C, Yang B, Huang G Y. Remote sensing image fusion based on multispectral image super-resolution[J]. Laser & Optoelectronics Progress, 53, 021001(2016).

    [4] Harris J L. Diffraction and resolving power[J]. Journal of the Optical Society of America, 54, 931-936(1964).

    [5] Li L, Sui L C, Kang J M et al. Super resolution reconstruction of remote sensing images based on online variational Bayesian estimation[J]. Laser & Optoelectronics Progress, 55, 062801(2018).

    [6] Xiao J S, Liu E N, Zhu L et al. Improved image super-resolution algorithm based on convolutional neural network[J]. Acta Optica Sinica, 37, 0318011(2017).

    [7] Freeman W T, Jones T R, Pasztor E C. Example-based super-resolution[J]. IEEE Computer Graphics and Applications, 22, 56-65(2002).

    [8] Chang H, Yeung D Y, Xiong Y M. Super-resolution through neighbor embedding. [C]∥IEEE Computer Society Conference on Computer Vision and Pattern Recognition, June 27-July 2, 2014, Washington, DC, USA. New York: IEEE, 275-282(2004).

    [9] 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). http://europepmc.org/abstract/MED/20483687

    [10] Dong C, Loy C C, He K M et al. Learning a deep convolutional network for image super-resolution. [C]∥Fleet D, Pajdla T, Schiele B, et al. European Conference on Computer Vision, Cham: Springer, 184-199(2014).

    [11] Moorthy A K, Bovik A C. A two-step framework for constructing blind image quality indices[J]. IEEE Signal Processing Letters, 17, 513-516(2010). http://ieeexplore.ieee.org/xpls/icp.jsp?arnumber=5432998

    [12] Moorthy A K, Bovik A C. Blind image quality assessment: from natural scene statistics to perceptual quality[J]. IEEE Transactions on Image Processing, 20, 3350-3364(2011). http://dl.acm.org/citation.cfm?id=2333947

    [13] Mittal A, Moorthy A K, Bovik A C. No-reference image quality assessment in the spatial domain[J]. IEEE Transactions on Image Processing, 21, 4695-4708(2012). http://www.ncbi.nlm.nih.gov/pubmed/22910118/

    [14] Saad M A, Bovik A C, Charrier C. A DCT statistics-based blind image quality index[J]. IEEE Signal Processing Letters, 17, 583-586(2010). http://ieeexplore.ieee.org/document/5430991

    [15] Saad M A, Bovik A C, Charrier C. Blind image quality assessment: A natural scene statistics approach in the DCT domain[J]. IEEE Transactions on Image Processing, 21, 3339-3352(2012). http://dl.acm.org/citation.cfm?id=2711713

    [16] Ye P, Kumar J, Kang L et al. Unsupervised feature learning framework for no-reference image quality assessment. [C]∥IEEE Conference on Computer Vision and Pattern Recognition, June 16-21, 2012, Providence, RI, USA. New York: IEEE, 1098-1105(2012).

    [17] Xue W F, Zhang L, Mou X Q. Learning without human scores for blind image quality assessment. [C]∥IEEE Conference on Computer Vision and Pattern Recognition, June 23-28, 2013, Portland, OR, USA. New York: IEEE, 995-1002(2013).

    [18] Mittal A, Soundararajan R, Bovik A C. Making a “completely blind” image quality analyzer[J]. IEEE Signal Processing Letters, 20, 209-212(2013). http://ieeexplore.ieee.org/document/6353522

    [19] Zhang L, Zhang L, Bovik A C. A feature-enriched completely blind image quality evaluator[J]. IEEE Transactions on Image Processing, 24, 2579-2591(2015). http://www.ncbi.nlm.nih.gov/pubmed/25915960

    [20] Ma C, Yang C Y, Yang X K et al. Learning a no-reference quality metric for single-image super-resolution[J]. Computer Vision and Image Understanding, 158, 1-16(2017). http://www.sciencedirect.com/science/article/pii/S107731421630203X

    [21] Ruderman D L. The statistics of natural images[J]. Network: Computation in Neural Systems, 5, 517-548(1994). http://www.tandfonline.com/doi/abs/10.1088/0954-898X_5_4_006

    [22] Ojala T, Pietikainen M, Maenpaa T. Multiresolution gray-scale and rotation invariant texture classification with local binary patterns[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 24, 971-987(2002). http://dl.acm.org/citation.cfm?id=628808

    [23] Breiman L. Random forests[J]. Machine Learning, 45, 5-32(2001). http://icb.oxfordjournals.org/external-ref?access_num=10.1023/A:1010933404324&link_type=DOI

    Song Xue, Siyu Zhang, Yongfeng Liu. Quality Assessment of Hyperspectral Super-Resolution Images[J]. Laser & Optoelectronics Progress, 2019, 56(4): 041001
    Download Citation