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Underwater Image Enhancement (UIE) aims to improve the visual quality from a low-quality input.
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C. Li, C. Guo, W. Ren, R. Cong, J. Hou, S. Kwong, and D. Tao, “An underwater image enhancement benchmark dataset and beyond,” IEEE Transactions on Image Processing , vol. 29, pp. 4376–4389, 2019
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M. J. Islam, Y. Xia, and J. Sattar, “Fast underwater image enhancement for improved visual perception,” IEEE Robotics and Automation Letters , vol. 5, no. 2, pp. 3227–3234, 2020
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D. Berman, D. Levy, S. Avidan, and T. Treibitz, “Underwater single image color restoration using haze-lines and a new quantitative dataset,” IEEE Transactions on Pattern Analysis and Machine Intelligence , vol. 43, no. 8, pp. 2822–2837, 2020
2020
Cited alongside, same era.
Z. Zhao, Y. Liu, X. Sun, J. Liu, X. Yang, and C. Zhou, “Composited fishnet: Fish detection and species recognition from low-quality underwater videos,” IEEE Transactions on Image Processing , vol. 30, pp. 4719–4734, 2021
2021
Cited alongside, same era.
C. Li, S. Anwar, J. Hou, R. Cong, C. Guo, and W. Ren, “Underwater image enhancement via medium transmission-guided multi-color space embedding,” IEEE Transactions on Image Processing , vol. 30, pp. 4985–5000, 2021
2021
Cited alongside, same era.
P. Zhuang, C. Li, and J. Wu, “Bayesian retinex underwater image enhancement,” Engineering Applications of Artificial Intelligence , vol. 101, p. 104171, 2021
2021
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O. Avrahami, D. Lischinski, and O. Fried, “Blended diffusion for text-driven editing of natural images,” in IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2022, pp. 18 208–18 218
2022
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2022
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A. Lugmayr, M. Danelljan, A. Romero, F. Yu, R. Timofte, and L. Van Gool, “Repaint: Inpainting using denoising diffusion probabilistic models,” in IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2022, pp. 11 461–11 471
2022
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Y. Wang, J. Guo, H. Gao, and H. Yue, “Uiecˆ 2-net: Cnn-based underwater image enhancement using two color space,” Signal Processing: Image Communication , vol. 96, p. 116250, 2021
2021
Cited alongside, same era.
J. Song, C. Meng, and S. Ermon, “Denoising diffusion implicit models,” International Conference on Learning Representations , 2021
2021
Cited alongside, same era.
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2021
Cited alongside, same era.
J. Ho and T. Salimans, “Classifier-free diffusion guidance,” in NeurIPS Workshop on Deep Generative Models and Downstream Applications , 2021
2021
Cited alongside, same era.
W. Zhang, L. Dong, T. Zhang, and W. Xu, “Enhancing underwater image via color correction and bi-interval contrast enhancement,” Signal Processing: Image Communication , vol. 90, p. 116030, 2021
2021
Cited alongside, same era.
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2021
Cited alongside, same era.
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2021
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C. Meng, Y. He, Y. Song, J. Song, J. Wu, J.-Y. Zhu, and S. Ermon, “SDEdit: Guided image synthesis and editing with stochastic differential equations,” in International Conference on Learning Representations , 2021
2021
Cited alongside, same era.
J. Zhou, Q. Liu, Q. Jiang, W. Ren, K.-M. Lam, and W. Zhang, “Underwater camera: Improving visual perception via adaptive dark pixel prior and color correction,” International Journal of Computer Vision , pp. 1–19, 2023
2023
Later among the works it cites.
K. Li, L. Wu, Q. Qi, W. Liu, X. Gao, L. Zhou, and D. Song, “Beyond single reference for training: Underwater image enhancement via comparative learning,” IEEE Transactions on Circuits and Systems for Video Technology , vol. 33, no. 6, pp. 2561–2576, 2023
2023
Later among the works it cites.
Z. Shen, H. Xu, T. Luo, Y. Song, and Z. He, “Udaformer: underwater image enhancement based on dual attention transformer,” Computers & Graphics , vol. 111, pp. 77–88, 2023
2023
Later among the works it cites.
2023
Later among the works it cites.
L. Zhang, A. Rao, and M. Agrawala, “Adding conditional control to text-to-image diffusion models,” in IEEE/CVF International Conference on Computer Vision , 2023, pp. 3836–3847
2023
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Z. Liang, C. Li, S. Zhou, R. Feng, and C. C. Loy, “Iterative prompt learning for unsupervised backlit image enhancement,” in IEEE/CVF International Conference on Computer Vision , 2023, pp. 8094–8103
2023
Later among the works it cites.
Y. Tang, H. Kawasaki, and T. Iwaguchi, “Underwater image enhancement by transformer-based diffusion model with non-uniform sampling for skip strategy,” in ACM International Conference on Multimedia , 2023, pp. 5419–5427
2023
Later among the works it cites.
Z. Luo, F. K. Gustafsson, Z. Zhao, J. Sjölund, and T. B. Schön, “Image restoration with mean-reverting stochastic differential equations,” in International Conference on Machine Learning , 2023, pp. 23 045–23 066
2023
Later among the works it cites.
Q. Jiang, Y. Kang, Z. Wang, W. Ren, and C. Li, “Perception-driven deep underwater image enhancement without paired supervision,” IEEE Transactions on Multimedia , vol. 26, pp. 4884–4897, 2024
2024
Closest in time.
Y. Rao, W. Liu, K. Li, H. Fan, S. Wang, and J. Dong, “Deep color compensation for generalized underwater image enhancement,” IEEE Transactions on Circuits and Systems for Video Technology , vol. 34, no. 4, pp. 2577–2590, 2024
2024
Closest in time.
W. Zhang, L. Zhou, P. Zhuang, G. Li, X. Pan, W. Zhao, and C. Li, “Underwater image enhancement via weighted wavelet visual perception fusion,” IEEE Transactions on Circuits and Systems for Video Technology , vol. 34, no. 4, pp. 2469–2483, 2024
2024
Closest in time.
K. Li, H. Fan, Q. Qi, C. Yan, K. Sun, and Q. M. J. Wu, “TCTL-Net: Template-free color transfer learning for self-attention driven underwater image enhancement,” IEEE Transactions on Circuits and Systems for Video Technology , vol. 34, no. 6, pp. 4682–4697, 2024
2024
Closest in time.
T. Zhang, H. Su, B. Fan, N. Yang, S. Zhong, and J. Yin, “Underwater image enhancement based on red channel correction and improved multiscale fusion,” IEEE Transactions on Geoscience and Remote Sensing , vol. 62, pp. 1–20, 2024
2024
Closest in time.
J. Zhou, S. Wang, Z. Lin, Q. Jiang, and F. Sohel, “A pixel distribution remapping and multi-prior retinex variational model for underwater image enhancement,” IEEE Transactions on Multimedia , vol. 26, pp. 7838–7849, 2024
2024
Closest in time.
O. Patashnik, Z. Wu, E. Shechtman, D. Cohen-Or, and D. Lischinski, “StyleCLIP: Text-driven manipulation of stylegan imagery,” in IEEE/CVF International Conference on Computer Vision , 2021, pp. 2085–2094
2094
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