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Despite its fruitful applications in remote sensing, image super-resolution is troublesome to train and deploy as it handles different resolution magnifications with separate models.
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2004
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Z. Wang, 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, no. 4, pp. 600–612, 2004
2004
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M. T. Merino and J. Nunez, “Super-resolution of remotely sensed images with variable-pixel linear reconstruction,” IEEE Transactions on Geoscience and Remote Sensing , vol. 45, no. 5, pp. 1446–1457, 2007
2007
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J. Yang, J. Wright, T. Huang, and Y. Ma, “Image super-resolution as sparse representation of raw image patches,” in 2008 IEEE conference on computer vision and pattern recognition . IEEE, 2008, pp. 1–8
2008
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T.-M. Chan, J. Zhang, J. Pu, and H. Huang, “Neighbor embedding based super-resolution algorithm through edge detection and feature selection,” Pattern Recognition Letters , vol. 30, no. 5, pp. 494–502, 2009
2009
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L. Zhang, H. Zhang, H. Shen, and P. Li, “A super-resolution reconstruction algorithm for surveillance images,” Signal Processing , vol. 90, no. 3, pp. 848–859, 2010
2010
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Y.-W. Tai, S. Liu, M. S. Brown, and S. Lin, “Super resolution using edge prior and single image detail synthesis,” in 2010 IEEE computer society conference on computer vision and pattern recognition . IEEE, 2010, pp. 2400–2407
2010
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J. Yang, J. Wright, T. S. Huang, and Y. Ma, “Image super-resolution via sparse representation,” IEEE transactions on image processing , vol. 19, no. 11, pp. 2861–2873, 2010
2010
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Y. Yang and S. Newsam, “Bag-of-visual-words and spatial extensions for land-use classification,” in Proceedings of the 18th SIGSPATIAL international conference on advances in geographic information systems , 2010, pp. 270–279
2010
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2014
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J. Xie, R. S. Feris, and M.-T. Sun, “Edge-guided single depth image super resolution,” IEEE Transactions on Image Processing , vol. 25, no. 1, pp. 428–438, 2015
2015
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Y. Zhang, J. Liu, W. Yang, and Z. Guo, “Image super-resolution based on structure-modulated sparse representation,” IEEE Transactions on Image Processing , vol. 24, no. 9, pp. 2797–2810, 2015
2015
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C. Dong, C. C. Loy, K. He, and X. Tang, “Image super-resolution using deep convolutional networks,” IEEE transactions on pattern analysis and machine intelligence , vol. 38, no. 2, pp. 295–307, 2015
2015
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J. Kim, J. K. Lee, and K. M. Lee, “Accurate image super-resolution using very deep convolutional networks,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2016, pp. 1646–1654
2016
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C. Dong, C. C. Loy, and X. Tang, “Accelerating the super-resolution convolutional neural network,” in European conference on computer vision . Springer, 2016, pp. 391–407
2016
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2016
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S. Lei, Z. Shi, and Z. Zou, “Super-resolution for remote sensing images via local–global combined network,” IEEE Geoscience and Remote Sensing Letters , vol. 14, no. 8, pp. 1243–1247, 2017
2017
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J. Yamanaka, S. Kuwashima, and T. Kurita, “Fast and accurate image super resolution by deep cnn with skip connection and network in network,” in International Conference on Neural Information Processing . Springer, 2017, pp. 217–225
2017
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B. Lim, S. Son, H. Kim, S. Nah, and K. Mu Lee, “Enhanced deep residual networks for single image super-resolution,” in Proceedings of the IEEE conference on computer vision and pattern recognition workshops , 2017, pp. 136–144
2017
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A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, Ł. Kaiser, and I. Polosukhin, “Attention is all you need,” Advances in neural information processing systems , vol. 30, 2017
2017
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G.-S. Xia, J. Hu, F. Hu, B. Shi, X. Bai, Y. Zhong, L. Zhang, and X. Lu, “Aid: A benchmark data set for performance evaluation of aerial scene classification,” IEEE Transactions on Geoscience and Remote Sensing , vol. 55, no. 7, pp. 3965–3981, 2017
2017
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Y. Zhang, Y. Tian, Y. Kong, B. Zhong, and Y. Fu, “Residual dense network for image super-resolution,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2018, pp. 2472–2481
2018
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Y. Zhang, K. Li, K. Li, L. Wang, B. Zhong, and Y. Fu, “Image super-resolution using very deep residual channel attention networks,” in Proceedings of the European conference on computer vision (ECCV) , 2018, pp. 286–301
2018
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D. Mahapatra, B. Bozorgtabar, and R. Garnavi, “Image super-resolution using progressive generative adversarial networks for medical image analysis,” Computerized Medical Imaging and Graphics , vol. 71, pp. 30–39, 2019
2019
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Y. Pang, J. Cao, J. Wang, and J. Han, “Jcs-net: Joint classification and super-resolution network for small-scale pedestrian detection in surveillance images,” IEEE Transactions on Information Forensics and Security , vol. 14, no. 12, pp. 3322–3331, 2019
2019
Cited alongside, same era.
H. Ji, Z. Gao, T. Mei, and B. Ramesh, “Vehicle detection in remote sensing images leveraging on simultaneous super-resolution,” IEEE Geoscience and Remote Sensing Letters , vol. 17, no. 4, pp. 676–680, 2019
2019
Cited alongside, same era.
S. Lei, Z. Shi, X. Wu, B. Pan, X. Xu, and H. Hao, “Simultaneous super-resolution and segmentation for remote sensing images,” in IGARSS 2019-2019 IEEE International Geoscience and Remote Sensing Symposium . IEEE, 2019, pp. 3121–3124
2019
Cited alongside, same era.
J. M. Haut, M. E. Paoletti, R. Fernandez-Beltran, J. Plaza, A. Plaza, and J. Li, “Remote sensing single-image superresolution based on a deep compendium model,” IEEE Geoscience and Remote Sensing Letters , vol. 16, no. 9, pp. 1432–1436, 2019
J. Xie, L. Fang, B. Zhang, J. Chanussot, and S. Li, “Super resolution guided deep network for land cover classification from remote sensing images,” IEEE Transactions on Geoscience and Remote Sensing , vol. 60, pp. 1–12, 2021
2021
Later among the works it cites.
Q. Zhang, G. Yang, and G. Zhang, “Collaborative network for super-resolution and semantic segmentation of remote sensing images,” IEEE Transactions on Geoscience and Remote Sensing , vol. 60, pp. 1–12, 2021
2021
Later among the works it cites.
K. Chen, Z. Zou, and Z. Shi, “Building extraction from remote sensing images with sparse token transformers,” Remote Sensing , vol. 13, no. 21, p. 4441, 2021
2021
Later among the works it cites.
L. Chen, H. Liu, M. Yang, Y. Qian, Z. Xiao, and X. Zhong, “Remote sensing image super-resolution via residual aggregation and split attentional fusion network,” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing , vol. 14, pp. 9546–9556, 2021
2021
Later among the works it cites.
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2019
Cited alongside, same era.
K. Jiang, Z. Wang, P. Yi, G. Wang, T. Lu, and J. Jiang, “Edge-enhanced gan for remote sensing image superresolution,” IEEE Transactions on Geoscience and Remote Sensing , vol. 57, no. 8, pp. 5799–5812, 2019
2019
Cited alongside, same era.
S. Lei, Z. Shi, and Z. Zou, “Coupled adversarial training for remote sensing image super-resolution,” IEEE Transactions on Geoscience and Remote Sensing , vol. 58, no. 5, pp. 3633–3643, 2019
2019
Cited alongside, same era.
X. Hu, H. Mu, X. Zhang, Z. Wang, T. Tan, and J. Sun, “Meta-sr: A magnification-arbitrary network for super-resolution,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , 2019, pp. 1575–1584
2019
Cited alongside, same era.
L. Courtrai, M.-T. Pham, and S. Lefèvre, “Small object detection in remote sensing images based on super-resolution with auxiliary generative adversarial networks,” Remote Sensing , vol. 12, no. 19, p. 3152, 2020
2020
Cited alongside, same era.
Z. Wang, J. Chen, and S. C. Hoi, “Deep learning for image super-resolution: A survey,” IEEE transactions on pattern analysis and machine intelligence , vol. 43, no. 10, pp. 3365–3387, 2020
2020
Cited alongside, same era.
C. Tian, R. Zhuge, Z. Wu, Y. Xu, W. Zuo, C. Chen, and C.-W. Lin, “Lightweight image super-resolution with enhanced cnn,” Knowledge-Based Systems , vol. 205, p. 106235, 2020
2020
Cited alongside, same era.
C. Tian, Y. Xu, W. Zuo, B. Zhang, L. Fei, and C.-W. Lin, “Coarse-to-fine cnn for image super-resolution,” IEEE Transactions on Multimedia , vol. 23, pp. 1489–1502, 2020
2020
Cited alongside, same era.
M. Zhang and Q. Ling, “Supervised pixel-wise gan for face super-resolution,” IEEE Transactions on Multimedia , vol. 23, pp. 1938–1950, 2020
2020
Cited alongside, same era.
S. Lei and Z. Shi, “Hybrid-scale self-similarity exploitation for remote sensing image super-resolution,” IEEE Transactions on Geoscience and Remote Sensing , vol. 60, pp. 1–10, 2021
2021
Later among the works it cites.
Y. Chen, S. Liu, and X. Wang, “Learning continuous image representation with local implicit image function,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , 2021, pp. 8628–8638
2021
Later among the works it cites.
2021
Later among the works it cites.
S. Lei, Z. Shi, and W. Mo, “Transformer-based multistage enhancement for remote sensing image super-resolution,” IEEE Transactions on Geoscience and Remote Sensing , vol. 60, pp. 1–11, 2021
2021
Later among the works it cites.
B. Liu, L. Zhao, J. Li, H. Zhao, W. Liu, Y. Li, Y. Wang, H. Chen, and W. Cao, “Saliency-guided remote sensing image super-resolution,” Remote Sensing , vol. 13, no. 24, p. 5144, 2021
2021
Later among the works it cites.
H. Shen, Z. Qiu, L. Yue, and L. Zhang, “Deep-learning-based super-resolution of video satellite imagery by the coupling of multiframe and single-frame models,” IEEE Transactions on Geoscience and Remote Sensing , vol. 60, pp. 1–14, 2021
2021
Later among the works it cites.
L. Wang, Y. Wang, Z. Lin, J. Yang, W. An, and Y. Guo, “Learning a single network for scale-arbitrary super-resolution,” in Proceedings of the IEEE/CVF international conference on computer vision , 2021, pp. 4801–4810
2021
Later among the works it cites.
N. Ni, H. Wu, and L. Zhang, “Hierarchical feature aggregation and self-learning network for remote sensing image continuous-scale super-resolution,” IEEE Geoscience and Remote Sensing Letters , vol. 19, pp. 1–5, 2021
2021
Later among the works it cites.
Y. Fu, J. Chen, T. Zhang, and Y. Lin, “Residual scale attention network for arbitrary scale image super-resolution,” Neurocomputing , vol. 427, pp. 201–211, 2021
2021
Later among the works it cites.
2021
Later among the works it cites.
B. Mildenhall, P. P. Srinivasan, M. Tancik, J. T. Barron, R. Ramamoorthi, and R. Ng, “Nerf: Representing scenes as neural radiance fields for view synthesis,” Communications of the ACM , vol. 65, no. 1, pp. 99–106, 2021
2021
Later among the works it cites.
P. Behjati, P. Rodriguez, A. Mehri, I. Hupont, C. F. Tena, and J. Gonzalez, “Overnet: Lightweight multi-scale super-resolution with overscaling network,” in Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision , 2021, pp. 2694–2703
2021
Later among the works it cites.
2021
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J. Chen, K. Chen, H. Chen, Z. Zou, and Z. Shi, “A degraded reconstruction enhancement-based method for tiny ship detection in remote sensing images with a new large-scale dataset,” IEEE Transactions on Geoscience and Remote Sensing , 2022
2022
Later among the works it cites.
Z. Lu, J. Li, H. Liu, C. Huang, L. Zhang, and T. Zeng, “Transformer for single image super-resolution,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2022, pp. 457–466
2022
Later among the works it cites.
Z. Wang, L. Li, Y. Xue, C. Jiang, J. Wang, K. Sun, and H. Ma, “Fenet: Feature enhancement network for lightweight remote-sensing image super-resolution,” IEEE Transactions on Geoscience and Remote Sensing , vol. 60, pp. 1–12, 2022
2022
Later among the works it cites.
K. Chen, W. Li, J. Chen, Z. Zou, and Z. Shi, “Resolution-agnostic remote sensing scene classification with implicit neural representations,” IEEE Geoscience and Remote Sensing Letters , 2022
2022
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H. Li, T. Dai, Y. Li, X. Zou, and S.-T. Xia, “Adaptive local implicit image function for arbitrary-scale super-resolution,” in 2022 IEEE International Conference on Image Processing (ICIP) . IEEE, 2022, pp. 4033–4037
2022
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Q. H. Nguyen and W. J. Beksi, “Single image super-resolution via a dual interactive implicit neural network,” in Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision , 2023, pp. 4936–4945
2023
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