[3] [3] Khaledyan D, Amirany A, Jafari K, et al. Low-cost implementation of bilinear and bicubic image interpolation for real-time image super-resolution[C]//2020 IEEE Global Humanitarian Technology Conference (GHTC). IEEE, 2020: 1-5.

[4] [4] Dong C, Loy C C, He K, et al. Learning a deep convolutional network for image super-resolution[C]//Computer Vision-ECCV 2014: 13th European Conference, Zurich, Switzerland, September 6-12, 2014.

[6] [6] Kim J, Lee J K, Lee K M. Deeply-recursive convolutional network for image super-resolution[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2016: 1637-1645.

[7] [7] Creswell A, White T, Dumoulin V, et al. Generative adversarial networks: an overview[J]. IEEE signal processing magazine, 2018, 35(1): 53-65.

[8] [8] Ledig C, Theis L, Huszr F, et al. Photo-realistic single image super-resolution using a generative adversarial network[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2017: 4681-4690.

[9] [9] Shaham T R, Dekel T, Michaeli T. Singan: Learning a generative model from a single natural image[C]//Proceedings of the IEEE/CVF International Conference on Computer Vision, 2019: 4570-4580.

[12] [12] Liu Z, Mao H, Wu C Y, et al. A convnet for the 2020s[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2022: 11976-11986.