# Denoising Multiscale Back-Projection Feature Fusion for Underwater Image Enhancement
## 6 Conclusions
(2023)_ [PERSON]; [PERSON]; [PERSON] Iterative Back Projection Network Based on Deformable 3D Convolution. _IEEE Access_**2023**, _11_, 122586-122597. [CrossRef]
* _[PERSON] et al. (2021)_ [PERSON]; [PERSON]; [PERSON]; [PERSON] Knowledge Distillation: A Survey. _Int. J. Comput. Vis._**2021**, _129_, 1789-1819. [CrossRef]
* _[PERSON] and [PERSON] (2022)_ [PERSON]; [PERSON] Knowledge Distillation and Student-Teacher Learning for Visual Intelligence: A Review and New Outlooks. _IEEE Trans. Pattern Anal. Mach. Intell._**2022**, _44_, 3048-3068. [CrossRef] [PubMed]
* _[PERSON] et al. (2020)_ [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] When Object Detection Meets Knowledge Distillation: A Survey. _IEEE Trans. Pattern Anal. Mach. Intell._**2023**, _45_, 10555-10579. [CrossRef] [PubMed]
* _[PERSON] et al. (2015)_ [PERSON]; [PERSON]; [PERSON] Distilling the Knowledge in a Neural Network. _arXiv_**2015**, arXiv:1503.02531. * _[PERSON] et al. (2024)_ [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Hierarchical Multi-Attention Transfer for Knowledge Distillation. _ACM Trans. Multim. Comput. Commun. Appl._**2024**, _20_, 51:1-51:20. [CrossRef]
* _[PERSON] et al. (2019)_ [PERSON]; [PERSON]; [PERSON] [PERSON] A Two-Teacher Framework for Knowledge Distillation. In Proceedings of the Advances in Neural Networks--ISNN 2019--16 th International Symposium on Neural Networks, ISNN 2019, Moscow, Russia, 10-12 July 2019; Proceedings, Part I; [PERSON], [PERSON], [PERSON], Eds. ; Lecture Notes in Computer Science; Springer: Berlin/Heidelberg, Germany, 2019; Volume 11554, pp. 58-66. * _[PERSON] and [PERSON] (2020)_ [PERSON]; [PERSON] Feature-Level Ensemble Knowledge Distillation for Aggregating Knowledge from Multiple Networks. In Proceedings of the ECAI 2020--24 th European Conference on Artificial Intelligence, Santiago de Compostela, Spain, 29 August-8 September 2020; pp. 1411-1418. * _[PERSON] et al. (2021)_ [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Reinforced Multi-Teacher Selection for Knowledge Distillation. In Proceedings of the Thirty-Fifth AAAI Conference on Artificial Intelligence, Virtual Event, 2-9 February 2021; pp. 14284-14291. * _[PERSON] et al. (2024)_ [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Knowledge Distillation via Multi-Teacher Feature Ensemble. _IEEE Signal Process. Lett._**2024**, _31_, 566-570. [CrossRef]
* _[PERSON] and [PERSON] (2015)_ [PERSON]; [PERSON] Boosting of Image Denoising Algorithms. _SIAM J. Imaging Sci._**2015**, \(8\), 1187-1219. [CrossRef]
* _[PERSON] et al. (2004)_ [PERSON]; [PERSON]; [PERSON] Image quality assessment: From error visibility to structural similarity. _IEEE Trans. Image Process._**2004**, _13_, 600-612. [CrossRef] [PubMed]
* _[PERSON] et al. (2019)_ [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Real-world Underwater Enhancement: Challenges, Benchmarks, and Solutions under Natural Light. _IEEE Trans. Circuits Syst. Video Technol._**2019**, _30_, 4861-4875. [CrossRef]
* _[PERSON] et al. (2017)_ [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Removing Rain from Single Images via a Deep Detail Network. In Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017, IEEE Computer Society, Honolulu, HI, USA, 21-26 July 2017; pp. 1715-1723. * _[PERSON] et al. (2019)_ [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Benchmarking Single-Image Dehazing and Beyond. _IEEE Trans. Image Process._**2019**, _28_, 492-505. [CrossRef] [PubMed]
* _[PERSON] et al. (2021)_ [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] [PERSON] ALL Snow Removed: Single Image Desnowing Algorithm Using Hierarchical Dual-tree Complex Wavelet Representation and Contradict Channel Loss. In Proceedings of the 2021 IEEE/CVF International Conference on Computer Vision, ICCV 2021, Montreal, QC, Canada, 10-17 October 2021; pp. 4176-4185. * _[PERSON] and [PERSON] (2009)_ [PERSON]; [PERSON] Mean squared error: Love it or leave it? A new look at Signal Fidelity Measures. _IEEE Signal Process.