Laser & Optoelectronics Progress, Volume. 56, Issue 4, 041001(2019)
Quality Assessment of Hyperspectral Super-Resolution Images
[4] Harris J L. Diffraction and resolving power[J]. Journal of the Optical Society of America, 54, 931-936(1964).
[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).
[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).
[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).
Get Citation
Copy Citation Text
Song Xue, Siyu Zhang, Yongfeng Liu. Quality Assessment of Hyperspectral Super-Resolution Images[J]. Laser & Optoelectronics Progress, 2019, 56(4): 041001
Category: Image Processing
Received: Aug. 23, 2018
Accepted: Aug. 31, 2018
Published Online: Jul. 31, 2019
The Author Email: Siyu Zhang (yusonzhang@foxmail.com), Yongfeng Liu (954271756@qq.com)