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Low-light image enhancement techniques have significantly progressed, but unstable image quality recovery and unsatisfactory visual perception are still significant challenges.
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M. Lamba, K. K. Rachavarapu, and K. Mitra, “Harnessing multi-view perspective of light fields for low-light imaging,” IEEE Transactions on Image Processing , vol. 30, pp. 1501–1513, 2020
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J. Ho, A. Jain, and P. Abbeel, “Denoising diffusion probabilistic models,” Advances in neural information processing systems , vol. 33, pp. 6840–6851, 2020
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S. Lim and W. Kim, “Dslr: Deep stacked laplacian restorer for low-light image enhancement,” IEEE Transactions on Multimedia , vol. 23, pp. 4272–4284, 2020
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G. Li, Y. Yang, X. Qu, D. Cao, and K. Li, “A deep learning based image enhancement approach for autonomous driving at night,” Knowledge-Based Systems , vol. 213, p. 106617, 2021
2021
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Y. Jiang, X. Gong, D. Liu, Y. Cheng, C. Fang, X. Shen, J. Yang, P. Zhou, and Z. Wang, “Enlightengan: Deep light enhancement without paired supervision,” IEEE transactions on image processing , vol. 30, pp. 2340–2349, 2021
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W. Wu, J. Weng, P. Zhang, X. Wang, W. Yang, and J. Jiang, “Uretinex-net: Retinex-based deep unfolding network for low-light image enhancement,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , 2022, pp. 5901–5910
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J. Hou, Z. Zhu, J. Hou, H. Liu, H. Zeng, and H. Yuan, “Global structure-aware diffusion process for low-light image enhancement,” Advances in Neural Information Processing Systems , vol. 36, 2024
2024
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