[1] Chan, K. C., Zhou, S., Xu, X., & Loy, C. C. (2022). Investigating tradeoffs in real-world video super-resolution. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 5962-5971).
[2] Yang, X., Xiang, W., Zeng, H., & Zhang, L. (2021). Real-world video super-resolution: A benchmark dataset and a decomposition-based learning scheme. In Proceedings of the IEEE/CVF International Conference on Computer Vision (pp. 4781-4790).
[3] Lee, J., Lee, M., Cho, S., & Lee, S. (2022). Reference-based video super-resolution using multi-camera video triplets. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition (pp. 17824-17833).
[4] Chiche, B. N., Woiselle, A., Frontera-Pons, J., & Starck, J. L. (2022). Stable long-term recurrent video super-resolution. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition (pp. 837-846).
[5] Chen, P., Yang, W., Wang, M., Sun, L., Hu, K., & Wang, S. (2021). Compressed domain deep video super-resolution. IEEE Transactions on Image Processing, 30, 7156-7169.
[6] Shi, S., Gu, J., Xie, L., Wang, X., Yang, Y., & Dong, C. (2022). Rethinking alignment in video super-resolution transformers. Advances in Neural Information Processing Systems, 35, 36081-36093.
[7] Isobe, T., Jia, X., Tao, X., Li, C., Li, R., Shi, Y., ... & Tai, Y. W. (2022). Look back and forth: Video super-resolution with explicit temporal difference modeling. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 17411-17420).
[8] Qiu, Z., Yang, H., Fu, J., & Fu, D. (2022, October). Learning spatiotemporal frequency-transformer for compressed video super-resolution. In European Conference on Computer Vision (pp. 257-273). Cham: Springer Nature Switzerland.
[9] Xiao, Z., Fu, X., Huang, J., Cheng, Z., & Xiong, Z. (2021). Space-time distillation for video super-resolution. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition (pp. 2113-2122).
[10] Zhang, A., Li, Q., Chen, Y., Ma, X., Zou, L., Jiang, Y., ... & Muntean, G. M. (2021). Video super-resolution and caching—An edge-assisted adaptive video streaming solution. IEEE Transactions on Broadcasting, 67(4), 799-812.
[11] Yu, J., Liu, J., Bo, L., & Mei, T. (2022). Memory-augmented non-local attention for video super-resolution. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition (pp. 17834-17843).
[12] Zhu, X., Li, Z., Lou, J., & Shen, Q. (2021). Video super-resolution based on a spatio-temporal matching network. Pattern Recognition, 110, 107619.
[13] Hu, M., Jiang, K., Wang, Z., Bai, X., & Hu, R. (2023). Cycmunet+: Cycle-projected mutual learning for spatial-temporal video super-resolution. IEEE Transactions on Pattern Analysis and Machine Intelligence.
[14] Wang, L., Hajiesmaili, M., & Sitaraman, R. K. (2021, October). Focas: Practical video super-resolution using foveated rendering. In Proceedings of the 29th ACM International Conference on Multimedia (pp. 5454-5462).
[15] Luo, L., Yi, B., Wang, Z., Yi, P., & He, Z. (2024). Efficient lightweight network for video super-resolution. Neural Computing and Applications, 36(2), 883-896.
[16] Lin, J., Huang, Y., & Wang, L. (2021). FDAN: Flow-guided deformable alignment network for video super-resolution. arXiv preprint arXiv: 2105.05640.
[17] Luo, J., Huang, S., & Yuan, Y. (2020, October). Video super-resolution using multi-scale pyramid 3d convolutional networks. In Proceedings of the 28th ACM International Conference on Multimedia (pp. 1882-1890).
[18] Armin Kappeler, Seunghwan Yoo, Qiqin Dai, and Aggelos K. Katsaggelos." Video Super-Resolution with Convolutional Neural Networks". IEEE Transactions on Computational Imaging 2016.
[19] Li, Tianyi, et al. "A Deep Learning Approach for Multi-Frame In-Loop Filter of HEVC." IEEE Transactions on Image Processing 28.11 (2019): 5663-5678.
[20] Liu, Dong, et al. "Deep Learning-Based Technology in Responses to the Joint Call for Proposals on Video Compression with Capability beyond HEVC." IEEE Transactions on Circuits and Systems for Video Technology (2019).
[21] Lin, Hongwei, et al. "Improved Low-Bitrate HEVC Video Coding using Deep Learning based Super-Resolution and Adaptive Block Patching." IEEE Transactions on Multimedia(2019).
[22] Wang, Y., Guo, J., Gao, H., & Yue, H. (2021). UIEC^ 2-Net: CNN-based underwater image enhancement using two color spaces. Signal Processing: Image Communication, 96, 116250.
[23] Magnusson, M., Sigurdsson, J., Armansson, S. E., Ulfarsson, M. O., Deborah, H., & Sveinsson, J. R. (2020, September). Creating RGB images from hyperspectral images using a color matching function. In IGARSS 2020-2020 IEEE International Geoscience and Remote Sensing Symposium (pp. 2045-2048). IEEE.
[24] Alwan, Z. A., Farhan, H. M., & Mahdi, S. Q. (2020). Color image steganography in YCbCr space. International Journal of Electrical and Computer Engineering, 10(1), 202.
[25] Ansari, M., & Singh, D. K. (2022). Significance of color spaces and their selection for image processing: a survey. Recent Advances in Computer Science and Communications (Formerly: Recent Patents on Computer Science), 15(7), 946-956.
[26] Saleem, E., & El Abbadi, N. K. (2020). Auto colorization of grayscale image using YCbCr color space. Iraqi Journal of Science, 3379-3386.
[27] Sahu, M., & Dash, R. (2021). A survey on deep learning: convolution neural network (CNN). In Intelligent and Cloud Computing: Proceedings of ICICC 2019, Volume 2 (pp. 317-325). Springer Singapore.
[28] Kumar, V., Choudhury, T., Satapathy, S. C., Tomar, R., & Aggarwal, A. (2020). Video super resolution using convolutional neural network and image fusion techniques. International Journal of Knowledge-based and Intelligent Engineering Systems, 24(4), 279-287.
[29] Daithankar, M. V., & Ruikar, S. D. (2020). Video super resolution: a review. In ICDSMLA 2019: Proceedings of the 1st International Conference on Data Science, Machine Learning and Applications (pp. 488-495). Springer Singapore.
[30] Khan, A., Sohail, A., Zahoora, U., & Qureshi, A. S. (2020). A survey of the recent architectures of deep convolutional neural networks. Artificial intelligence review, 53, 5455-5516.
[31] Sekehravani, E. A., Babulak, E., & Masoodi, M. (2020). Implementing canny edge detection algorithm for noisy image. Bulletin of Electrical Engineering and Informatics, 9(4), 1404-1410.
[32] Cao, Y., Wu, D., & Duan, Y. (2020). A new image edge detection algorithm based on improved Canny. Journal of Computational Methods in Sciences and Engineering, 20(2), 629-642.
[33] Sidén, P., & Lindsten, F. (2020, November). Deep Gaussian Markov random fields. In International conference on machine learning (pp. 8916-8926). PMLR.
[34] Blake, A., Kohli, P., & Rother, C. (Eds.). (2011). Markov random fields for vision and image processing. MIT press.
[35] Geman, S., & Graffigne, C. (1986, August). Markov random field image models and their applications to computer vision. In Proceedings of the international congress of mathematicians (Vol. 1, p. 2).
[36] Kim, J., J. K. Lee, and K. M. Lee. "Accurate Image Super-Resolution Using Very Deep Convolutional Networks." Proceedings of the IEEE® Conference on Computer Vision and Pattern Recognition. 2016, pp. 1646-1654.
[37] Xue, T., Chen, B., Wu, J., Wei, D., & Freeman, W. T. (2017). Video Enhancement with Task-Oriented Flow. arXiv.
[38] Zamzam, P., Rezaei, P., Khatami, S. A., & Appasani, B. (2025). Super perfect polarization-insensitive graphene disk terahertz absorber for breast cancer detection using deep learning. Optics & Laser Technology, 183, 112246.
[39] Cao, Y., Wang, C., Song, C., Tang, Y., & Li, H. (2021, July). Real-time super-resolution system of 4k-video based on deep learning. In 2021 IEEE 32nd International Conference on Application-specific Systems, Architectures and Processors (ASAP) (pp. 69-76). IEEE.
[40] Pan, J., Bai, H., Dong, J., Zhang, J., & Tang, J. (2021). Deep blind video super-resolution. In Proceedings of the IEEE/CVF international conference on computer vision (pp. 4811-4820).
[41] Li, F., Bai, H., & Zhao, Y. (2020). Learning a deep dual attention network for video super-resolution. IEEE transactions on image processing, 29, 4474-4488.
[42] Isobe, T., Li, S., Jia, X., Yuan, S., Slabaugh, G., Xu, C., ... & Tian, Q. (2020). Video super-resolution with temporal group attention. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition (pp. 8008-8017).
[43] Bai, H., & Pan, J. (2024). Self-supervised deep blind video super-resolution. IEEE Transactions on Pattern Analysis and Machine Intelligence.
[44] Feng, Z., Zhang, W., Liang, S., & Yu, Q. (2023). Deep video super-resolution using a hybrid imaging system. IEEE Transactions on Circuits and Systems for Video Technology, 33(9), 4855-4867.
[45] Wang, W., Liu, Z., Lu, H., Lan, R., & Zhang, Z. (2023). Real-Time Video Super-Resolution with Spatio-Temporal Modeling and Redundancy-Aware Inference. Sensors, 23(18), 7880.