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Mainstream approaches to spectral reconstruction (SR) primarily focus on designing Convolution- and Transformer-based architectures.
J. Canny, “A computational approach to edge detection,” IEEE Transactions on pattern analysis and machine intelligence , no. 6, pp. 679–698, 1986
1986
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2010
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F. Yasuma, T. Mitsunaga, D. Iso, and S. K. Nayar, “Generalized assorted pixel camera: postcapture control of resolution, dynamic range, and spectrum,” IEEE transactions on image processing , vol. 19, no. 9, pp. 2241–2253, 2010
2010
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A. Chakrabarti and T. Zickler, “Statistics of real-world hyperspectral images,” in CVPR 2011 . IEEE, 2011, pp. 193–200
2011
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O. Ronneberger, P. Fischer, and T. Brox, “U-net: Convolutional networks for biomedical image segmentation,” in Medical image computing and computer-assisted intervention–MICCAI 2015: 18th international conference, Munich, Germany, October 5-9, 2015, proceedings, part III 18 . Springer, 2015, pp. 234–241
2015
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B. Arad and O. Ben-Shahar, “Sparse recovery of hyperspectral signal from natural rgb images,” in Computer Vision–ECCV 2016: 14th European Conference, Amsterdam, The Netherlands, October 11–14, 2016, Proceedings, Part VII 14 . Springer, 2016, pp. 19–34
2016
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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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Y. Liu, X. Yuan, J. Suo, D. J. Brady, and Q. Dai, “Rank minimization for snapshot compressive imaging,” IEEE transactions on pattern analysis and machine intelligence , vol. 41, no. 12, pp. 2990–3006, 2018
2018
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K. Fotiadou, G. Tsagkatakis, and P. Tsakalides, “Spectral super resolution of hyperspectral images via coupled dictionary learning,” IEEE Transactions on Geoscience and Remote Sensing , vol. 57, no. 5, pp. 2777–2797, 2018
2018
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N. Akhtar and A. Mian, “Hyperspectral recovery from rgb images using gaussian processes,” IEEE transactions on pattern analysis and machine intelligence , vol. 42, no. 1, pp. 100–113, 2018
2018
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T. Stiebel, S. Koppers, P. Seltsam, and D. Merhof, “Reconstructing spectral images from rgb-images using a convolutional neural network,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops , 2018, pp. 948–953
2018
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X.-H. Han, B. Shi, and Y. Zheng, “Residual hsrcnn: Residual hyper-spectral reconstruction cnn from an rgb image,” in 2018 24th International Conference on Pattern Recognition (ICPR) . IEEE, 2018, pp. 2664–2669
2018
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Y. Yan, L. Zhang, J. Li, W. Wei, and Y. Zhang, “Accurate spectral super-resolution from single rgb image using multi-scale cnn,” in Pattern Recognition and Computer Vision: First Chinese Conference, PRCV 2018, Guangzhou, China, November 23-26, 2018, Proceedings, Part II 1 . Springer, 2018, pp. 206–217
2018
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S. Woo, J. Park, J.-Y. Lee, and I. S. Kweon, “Cbam: Convolutional block attention module,” in Proceedings of the European conference on computer vision (ECCV) , 2018, pp. 3–19
2018
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Y. Geng, S. Mei, J. Tian, Y. Zhang, and Q. Du, “Spatial constrained hyperspectral reconstruction from rgb inputs using dictionary representation,” in IGARSS 2019-2019 IEEE International Geoscience and Remote Sensing Symposium . IEEE, 2019, pp. 3169–3172
2019
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U. B. Gewali, S. T. Monteiro, and E. Saber, “Spectral super-resolution with optimized bands,” Remote Sensing , vol. 11, no. 14, p. 1648, 2019
2019
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B. Kaya, Y. B. Can, and R. Timofte, “Towards spectral estimation from a single rgb image in the wild,” in 2019 IEEE/CVF International Conference on Computer Vision Workshop (ICCVW) . IEEE, 2019, pp. 3546–3555
2019
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L. Gao, D. Hong, J. Yao, B. Zhang, P. Gamba, and J. Chanussot, “Spectral superresolution of multispectral imagery with joint sparse and low-rank learning,” IEEE Transactions on Geoscience and Remote Sensing , vol. 59, no. 3, pp. 2269–2280, 2020
2020
Cited alongside, same era.
Y. Zhao, L.-M. Po, Q. Yan, W. Liu, and T. Lin, “Hierarchical regression network for spectral reconstruction from rgb images,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops , 2020, pp. 422–423
2020
Cited alongside, same era.
C. Ma, Y. Rao, Y. Cheng, C. Chen, J. Lu, and J. Zhou, “Structure-preserving super resolution with gradient guidance,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , 2020, pp. 7769–7778
2020
Cited alongside, same era.
L. Yan, X. Wang, M. Zhao, M. Kaloorazi, J. Chen, and S. Rahardja, “Reconstruction of hyperspectral data from rgb images with prior category information,” IEEE Transactions on Computational Imaging , vol. 6, pp. 1070–1081, 2020
2022
Later among the works it cites.
Y. Cai, J. Lin, Z. Lin, H. Wang, Y. Zhang, H. Pfister, R. Timofte, and L. Van Gool, “Mst++: Multi-stage spectral-wise transformer for efficient spectral reconstruction,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2022, pp. 745–755
2022
Later among the works it cites.
S. P. Ang, S. L. Phung, L. Bui, and A. Bouzerdoum, “Adaptornas: A new perturbation-based neural architecture search for hyperspectral image segmentation,” IEEE Transactions on Circuits and Systems for Video Technology , 2023
2023
Later among the works it cites.
H. Su, F. Shao, Y. Gao, H. Zhang, W. Sun, and Q. Du, “Probabilistic collaborative representation based ensemble learning for classification of wetland hyperspectral imagery,” IEEE Transactions on Geoscience and Remote Sensing , 2023
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2020
Cited alongside, same era.
2020
Cited alongside, same era.
B. Arad, R. Timofte, O. Ben-Shahar, Y.-T. Lin, and G. D. Finlayson, “Ntire 2020 challenge on spectral reconstruction from an rgb image,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops , 2020, pp. 446–447
2020
Cited alongside, same era.
Y. Cai, X. Hu, H. Wang, Y. Zhang, H. Pfister, and D. Wei, “Learning to generate realistic noisy images via pixel-level noise-aware adversarial training,” Advances in Neural Information Processing Systems , vol. 34, pp. 3259–3270, 2021
2021
Cited alongside, same era.
Z. Zhu, H. Liu, J. Hou, H. Zeng, and Q. Zhang, “Semantic-embedded unsupervised spectral reconstruction from single rgb images in the wild,” in Proceedings of the IEEE/CVF International Conference on Computer Vision , 2021, pp. 2279–2288
2021
Cited alongside, same era.
W. Chen, X. Zheng, and X. Lu, “Semisupervised spectral degradation constrained network for spectral super-resolution,” IEEE Geoscience and Remote Sensing Letters , vol. 19, pp. 1–5, 2021
2021
Cited alongside, same era.
J. Li, C. Wu, R. Song, Y. Li, W. Xie, L. He, and X. Gao, “Deep hybrid 2-d–3-d cnn based on dual second-order attention with camera spectral sensitivity prior for spectral super-resolution,” IEEE Transactions on Neural Networks and Learning Systems , vol. 34, no. 2, pp. 623–634, 2021
2021
Cited alongside, same era.
A. Gu, I. Johnson, K. Goel, K. Saab, T. Dao, A. Rudra, and C. Ré, “Combining recurrent, convolutional, and continuous-time models with linear state space layers,” Advances in neural information processing systems , vol. 34, pp. 572–585, 2021
2021
Cited alongside, same era.
2021
Cited alongside, same era.
2023
Later among the works it cites.
H. Chen, W. Zhao, T. Xu, G. Shi, S. Zhou, P. Liu, and J. Li, “Spectral-wise implicit neural representation for hyperspectral image reconstruction,” IEEE Transactions on Circuits and Systems for Video Technology , 2023
2023
Later among the works it cites.
C. Wu, J. Li, R. Song, Y. Li, and Q. Du, “Repcpsi: Coordinate-preserving proximity spectral interaction network with reparameterization for lightweight spectral super-resolution,” IEEE Transactions on Geoscience and Remote Sensing , vol. 61, pp. 1–13, 2023
2023
Later among the works it cites.
2023
Later among the works it cites.
J. He, Q. Yuan, J. Li, Y. Xiao, D. Liu, H. Shen, and L. Zhang, “Spectral super-resolution meets deep learning: Achievements and challenges,” Information Fusion , p. 101812, 2023
2023
Later among the works it cites.
Q. Tian, C. He, Y. Xu, Z. Wu, and Z. Wei, “Hyperspectral target detection: Learning faithful background representations via orthogonal subspace-guided variational autoencoder,” IEEE Transactions on Geoscience and Remote Sensing , 2024
2024
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