References

[1] D. Glasner, S. Bagon and M. Irani,  "Super-resolution from a single image," in 2009 IEEE 12th International Conference on Computer Vision (ICCV), Kyoto, 2009 pp. 349-356.

doi: 10.1109/ICCV.2009.5459271

[2] Yang, Wenming et al. “Deep Learning for Single Image Super-Resolution: A Brief Review.” IEEE Transactions on Multimedia 21.12 (2019): 3106–3121. Crossref. Web.

[3] J. Kim, J. Kwon Lee, and K. Mu Lee. Accurate image super-resolution using very deep convolutional networks. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 1646–1654, 2016.

[4]https://www.mathworks.com/help/images/single-image-super-resolution-using-deep-learning.html#VeryDeepSuperResolutionDeepLearningExample-15

[5] B. Lim, S. Son, H. Kim, S. Nah, and K. M. Lee. Enhanced deep residual networks for single image super-resolution. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, pages 1132–1140, 2017

[6] Yang, Wenming, Xuechen Zhang, Yapeng Tian, Wei Wang, Jing-Hao Xue, and Qingmin Liao. “Deep Learning for Single Image Super-Resolution: A Brief Review.” IEEE Transactions on Multimedia 21 (2018): 3106-3121.

[7] Zhou Wang, Alan C. Bovik, Hamid R. Sheikh, Eero P. Simoncelli.”Image Quality Assessment: From Error Visibility to Structural Similarity”.IEEE TRANSACTIONS ON IMAGE PROCESSING(2004).

[8] https://ece.uwaterloo.ca/~z70wang/publications/ssim.pdf

[9] https://github.com/deterenkelt/Nadeshiko/wiki/Docs-on-encoding.-SSIM-and-PSNR

[10]https://towardsdatascience.com/review-vdsr-super-resolution-f8050d49362f

[11] https://kornel.ski/en/faircomparison

[12] https://cv.snu.ac.kr/research/VDSR/VDSR_CVPR2016.pdf

[13] Zhou, W., A. C. Bovik, H. R. Sheikh, and E. P. Simoncelli. "Image Quality Assessment: From Error Visibility to Structural Similarity." IEEE Transactions on Image Processing. Vol. 13, Issue 4, April 2004, pp. 600–612.

[14] He, K., X. Zhang, S. Ren, and J. Sun. "Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification." Proceedings of the IEEE International Conference on Computer Vision, 2015, pp. 1026-1034.

[15]https://www.mathworks.com/help/images/single-image-super-resolution-using-deep-learning.html#mw_rtc_VeryDeepSuperResolutionDeepLearningExample_705D7EC3

[16]http://openaccess.thecvf.com/content_cvpr_2017_workshops/w12/papers/Lim_Enhanced_Deep_Residual_CVPR_2017_paper.pdf

[17] http://www.imatest.com/docs/ssim/

[18] Computer Vision – ECCV 2018: 15th European Conference, Munich, Germany, September 8–14, 2018, Proceedings, Part 9 by Vittorio Ferrari, Martial Hebert, Cristian Sminchisescu, Yair Weiss

[19] https://arxiv.org/pdf/1511.04587.pdf

[20]C. Dong, C. C. Loy, K. He, and X. Tang. Image superresolution using deep convolutional networks. TPAMI, 2015.

[21] https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6679528/

[22]https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6679528/

[23]https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6679528/table/sensors-19-03234-t013/

[24]https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6679528/figure/sensors-19-03234-f009/

[25] L. Zhou, Z. Wang, S. Wang, and Y. Luo, “Coarse-to-Fine Image Super-Resolution Using Convolutional Neural Networks,” in: MultiMedia Modeling. MMM 2018. Lecture Notes in Computer Science, Schoeffmann K. et al. (eds.). Springer, 2018, pp. 73–81. https://doi.org/10.1007/978-3-319-73600-6_7

[26] Romanuke, Vadim. (2019). An Improvement of the VDSR Network for Single Image Super-Resolution by Truncation and Adjustment of the Learning Rate Parameters. Applied Computer Systems. 24. 61-68. 10.2478/acss-2019-0008. 

[27] Deep Image Super Resolution via Natural Image Priors by Hojjat S. Mousavi, Tiantong Guo, Vishal Monga