[1]
|
Chen, H., He, X., Qing, L., et al. (2022) Real-World Single Image Super-Resolution: A Brief Review. Information Fu-sion, 79, 124-145. https://doi.org/10.1016/j.inffus.2021.09.005
|
[2]
|
Lepcha, D.C., Goyal, B., Dogra, A., et al. (2023) Image Super-Resolution: A Comprehensive Review, Recent Trends, Challenges and Applications. Information Fusion, 91, 230-260. https://doi.org/10.1016/j.inffus.2022.10.007
|
[3]
|
Wang, P., Bayram, B. and Sertel, E. (2022) A Comprehensive Review on Deep Learning Based Remote Sensing Image Super-Resolution Methods. Earth-Science Reviews, 2022, Article ID: 104110.
https://doi.org/10.1016/j.earscirev.2022.104110
|
[4]
|
Wang, L., Li, D., Zhu, Y., et al. (2020) Dual Su-per-Resolution Learning for Semantic Segmentation. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, Seattle, 14-19 June 2020, 3774-3783.
https://doi.org/10.1109/CVPR42600.2020.00383
|
[5]
|
Chen, M.J., Huang, C.H. and Lee, W.L. (2005) A Fast Edge-Oriented Algorithm for Image Interpolation. Image and Vision Computing, 23, 791-798. https://doi.org/10.1016/j.imavis.2005.05.005
|
[6]
|
Tom, B.C. and Katsaggelos, A.K. (1995) Reconstruction of a High-Resolution Image by Simultaneous Registration, Restoration, and Interpolation of Low-Resolution Images. Pro-ceedings IEEE International Conference on Image Processing, Vol. 2, 539-542.
|
[7]
|
Wang, Z., Chen, J. and Hoi, S.C.H. (2020) Deep Learning for Image Super-Resolution: A Survey. IEEE Transactions on Pattern Analysis and Ma-chine Intelligence, 43, 3365-3387. https://doi.org/10.1109/TPAMI.2020.2982166
|
[8]
|
Yang, W., Zhang, X., Tian, Y., et al. (2019) Deep Learning for Single Image Super-Resolution: A Brief Review. IEEE Transactions on Multimedia, 21, 3106-3121. https://doi.org/10.1109/TMM.2019.2919431
|
[9]
|
Liu, A., Liu, Y., Gu, J., et al. (2022) Blind Im-age Super-Resolution: A Survey and Beyond. IEEE Transactions on Pattern Analysis and Machine Intelligence, 45, 5461-5480. https://doi.org/10.1109/TPAMI.2022.3203009
|
[10]
|
Arefin, M.R., Michalski, V., St-Charles, P.L., et al. (2020) Multi-Image Super-Resolution for Remote Sensing Using Deep Recurrent Networks. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, Seattle, 14-19 June 2020, 206-207.
|
[11]
|
Dong, C., Loy, C.C., He, K., et al. (2014) Learning a Deep Convolutional Network for Image Su-per-Resolution. In: European Conference on Computer Vision, Springer, Cham, 184-199. https://doi.org/10.1007/978-3-319-10593-2_13
|
[12]
|
Krizhevsky, A., Sutskever, I. and Hinton, G.E. (2012) Imagenet Classification with Deep Convolutional Neural Networks. 26th Annual Conference on Neural Information Processing Systems, Lake Tahoe, 3-6 December 2012, 1097-1105.
|
[13]
|
Liu, J., Zou, M., Tang, J., et al. (2020) Memory Recursive Network for Single Image Super-Resolution. Proceedings of the 28th ACM International Conference on Mul-timedia, Seattle, 12-16 October 2020, 2202-2210.
https://doi.org/10.1145/3394171.3413696
|
[14]
|
Liu, F., Yang, X. and De Baets, B. (2023) A Deep Recursive Mul-ti-Scale Feature Fusion Network for Image Super-Resolution. Journal of Visual Communication and Image Representa-tion, 90, Article ID: 103730.
https://doi.org/10.1016/j.jvcir.2022.103730
|
[15]
|
He, K., Zhang, X., Ren, S., et al. (2016) Deep Residual Learning for Image Recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, 27-30 June 2016, 770-778.
https://doi.org/10.1109/CVPR.2016.90
|
[16]
|
Kim, J., Lee, J.K. and Lee, K.M. (2016) Accurate Image Su-per-Resolution Using Very Deep Convolutional Networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, 27-30 June 2016, 1646-1654. https://doi.org/10.1109/CVPR.2016.182
|
[17]
|
Li, Z., Liu, Y., Chen, X., et al. (2022) Blueprint Separable Residual Network for Efficient Image Super-Resolution. Proceedings of the IEEE/CVF Conference on Computer Vision and Pat-tern Recognition, New Orleans, 18-24 June 2022, 833-843. https://doi.org/10.1109/CVPRW56347.2022.00099
|
[18]
|
Gendy, G., Sabor, N., Hou, J., et al. (2023) Mixer-Based Local Residual Network for Lightweight Image Super-Resolution. Proceedings of the IEEE/CVF Conference on Com-puter Vision and Pattern Recognition, Vancouver, 18-22 June 2023, 1593-1602. https://doi.org/10.1109/CVPRW59228.2023.00161
|
[19]
|
Song, D., Xu, C., Jia, X., et al. (2020) Efficient Residual Dense Block Search for Image Super-Resolution. Proceedings of the AAAI Conference on Artificial Intelligence, 34, 12007-12014. https://doi.org/10.1609/aaai.v34i07.6877
|
[20]
|
Tong, T., Li, G., Liu, X., et al. (2017) Image Su-per-Resolution Using Dense Skip Connections. Proceedings of the IEEE International Conference on Computer Vision, Venice, 22-29 October 2017, 4799-4807.
https://doi.org/10.1109/ICCV.2017.514
|
[21]
|
Lv, X., Wang, C., Fan, X., et al. (2022) A Novel Image Su-per-Resolution Algorithm Based on Multi-Scale Dense Recursive Fusion Network. Neurocomputing, 489, 98-111. https://doi.org/10.1016/j.neucom.2022.02.042
|
[22]
|
Tian, C., Zhang, Y., Zuo, W., et al. (2022) A Heterogeneous Group CNN for Image Super-Resolution. IEEE Transactions on Neural Networks and Learning Sys-tems.
|
[23]
|
Ruangsang, W., Aramvith, S. and Onoye, T. (2023) Multi-FusNet of Cross Channel Network for Image Su-per-Resolution. IEEE Access, 11, 56287-56299. https://doi.org/10.1109/ACCESS.2023.3282571
|
[24]
|
Li, Y., Iwamoto, Y., Lin, L., et al. (2020) Parallel-Connected Residual Channel Attention Network for Remote Sensing Image Super-Resolution. Proceedings of the Asian Conference on Computer Vision, Kyoto, 30 November 2020 - 4 December 2020, 18-30.
|
[25]
|
Yang, Y. and Qi, Y. (2021) Image Super-Resolution via Channel Attention and Spatial Graph Con-volutional Network. Pattern Recognition, 112, Article ID: 107798. https://doi.org/10.1016/j.patcog.2020.107798
|
[26]
|
Zhang, X., Zeng, H., Guo, S., et al. (2022) Efficient Long-Range Attention Network for Image Super-Resolution. In: European Conference on Computer Vision, Springer Nature, Cham, 649-667.
https://doi.org/10.1007/978-3-031-19790-1_39
|
[27]
|
Yoo, J., Kim, T., Lee, S., et al. (2023) Enriched CNN-Transformer Feature Aggregation Networks for Super-Resolution. Proceedings of the IEEE/CVF Winter Confer-ence on Applications of Computer Vision, Waikoloa, 3-7 January 2023, 4956-4965. https://doi.org/10.1109/WACV56688.2023.00493
|
[28]
|
Wang, Z., Zhang, Z., Zhang, X., et al. (2023) DR2: Diffu-sion-Based Robust Degradation Remover for Blind Face Restoration. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, Vancouver, 18-22 June 2023, 1704-1713. https://doi.org/10.1109/CVPR52729.2023.00170
|
[29]
|
Zhang, K., Gool, L.V. and Timofte, R. (2020) Deep Un-folding Network for Image Super-Resolution. Proceedings of the IEEE/CVF Conference on Computer Vision and Pat-tern Recognition, Seattle, 14-19 June 2020, 3217-3226.
https://doi.org/10.1109/CVPR42600.2020.00328
|
[30]
|
Chen, X., Zhang, J., Xu, C., et al. (2023) Better “CMOS” Produces Clearer Images: Learning Space-Variant Blur Estimation for Blind Image Super-Resolution. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, Vancouver, 18-22 June 2023, 1651-1661. https://doi.org/10.1109/CVPR52729.2023.00165
|
[31]
|
Lee, R., Li, R., Venieris, S., et al. (2024) Meta-Learned Kernel for Blind Super-Resolution Kernel Estimation. Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, Waikoloa, 4-8 January 2024, 1496-1505.
|
[32]
|
Wei, Y., Gu, S., Li, Y., et al. (2021) Unsupervised Real-World Image Super Resolution via Domain-Distance Aware Training. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 19-25 June 2021, 13385-13394. https://doi.org/10.1109/CVPR46437.2021.01318
|
[33]
|
Zhou, H., Zhu, X., Zhu, J., et al. (2023) Learning Correction Filter via Degradation-Adaptive Regression for Blind Single Image Super-Resolution. Proceedings of the IEEE/CVF In-ternational Conference on Computer Vision, Paris, 2-6 October 2023, 12365-12375. https://doi.org/10.1109/ICCV51070.2023.01136
|
[34]
|
Weng, S.Y., Yuan, H., Xu, Y.S., et al. (2024) Best of both Worlds: Learning Arbitrary-Scale Blind Super-Resolution via Dual Degradation Representations and Cycle-Consistency. Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, Waikoloa, 4-8 January 2024, 1547-1556.
|
[35]
|
Zanotta, D.C., Junior, A.M., Motta, J.G., et al. (2023) An Assisted Multi-Frame Approach for Su-per-Resolution in Hyperspectral Images of Rock Samples. Computers & Geosciences, 181, Article ID: 105456.
https://doi.org/10.1016/j.cageo.2023.105456
|
[36]
|
Lu, L., Li, W., Tao, X., et al. (2021) Masa-Sr: Matching Acceler-ation and Spatial Adaptation for Reference-Based Image Super-Resolution. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 19-25 June 2021, 6368-6377. https://doi.org/10.1109/CVPR46437.2021.00630
|
[37]
|
Ibrahim, M.R., Benavente, R., Lumbreras, F., et al. (2022) 3DRRDB: Super Resolution of Multiple Remote Sensing Images Using 3D Residual in Residual Dense Blocks. Pro-ceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, New Orleans, 18-24 June 2022, 323-332. https://doi.org/10.1109/CVPRW56347.2022.00047
|
[38]
|
Tsai, R.Y. and Huang, T.S. (1984) Multiframe Image Restoration and Registration. In: Huang, T.S., Ed., Advances in Computer Vision and Image Processing, JAI Press Inc., Greenwich, 317-339.
|
[39]
|
Bhat, G., Danelljan, M., Yu, F., et al. (2021) Deep Reparametrization of Mul-ti-Frame Super-Resolution and Denoising. Proceedings of the IEEE/CVF International Conference on Computer Vision, 11-17 October 2021, 2460-2470.
https://doi.org/10.1109/ICCV48922.2021.00246
|
[40]
|
Mehta, N., Dudhane, A., Murala, S., et al. (2022) Adaptive Feature Consolidation Network for Burst Super-Resolution. Proceedings of the IEEE/CVF Conference on Computer Vi-sion and Pattern Recognition, New Orleans, 18-24 June 2022, 1279-1286. https://doi.org/10.1109/CVPRW56347.2022.00134
|
[41]
|
Wei, P., Sun, Y., Guo, X., et al. (2023) Towards Re-al-World Burst Image Super-Resolution: Benchmark and Method. Proceedings of the IEEE/CVF International Confer-ence on Computer Vision, Paris, 2-6 October 2023, 13233-13242.
https://doi.org/10.1109/ICCV51070.2023.01217
|
[42]
|
Zhang, Z., Wang, Z., Lin, Z., et al. (2019) Image Su-per-Resolution by Neural Texture Transfer. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, 15-20 June 2019, 7982-7991.
https://doi.org/10.1109/CVPR.2019.00817
|
[43]
|
Yang, F., Yang, H., Fu, J., et al. (2020) Learning Texture Trans-former Network for Image Super-Resolution. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, Seattle, 14-19 June 2020, 5791-5800.
https://doi.org/10.1109/CVPR42600.2020.00583
|
[44]
|
Jiang, Y., Chan, K.C., Wang, X., Loy, C.C. and Liu, Z. (2021) Robust Reference-Based Super-Resolution via C2-Matching. IEEE Conference on Computer Vision and Pattern Recognition, 19-25 June 2021, 2103-2112.
https://doi.org/10.1109/CVPR46437.2021.00214
|
[45]
|
Cao, J., Liang, J., Zhang, K., et al. (2022) Reference-Based Image Super-Resolution with Deformable Attention Transformer. In: European Conference on Computer Vision, Springer Nature, Cham, 325-342.
https://doi.org/10.1007/978-3-031-19797-0_19
|