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In the field of low-light image enhancement, both traditional Retinex methods and advanced deep learning techniques such as Retinexformer have shown distinct advantages and limitations.
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Jobson, D.J., Rahman, Z.u., Woodell, G.A.: A multiscale retinex for bridging the gap between color images and the human observation of scenes. IEEE Transactions on Image processing 6
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Jobson, D.J., Rahman, Z.u., Woodell, G.A.: Properties and performance of a center/surround retinex. IEEE transactions on image processing 6
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Fu, X., Zeng, D., Huang, Y., Zhang, X.P., Ding, X.: A weighted variational model for simultaneous reflectance and illumination estimation. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp. 2782–2790 (2016)
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Guo, X., Li, Y., Ling, H.: Lime: Low-light image enhancement via illumination map estimation. IEEE Transactions on image processing 26
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He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp. 770–778 (2016)
2016
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Rahman, S., Rahman, M.M., Abdullah-Al-Wadud, M., Al-Quaderi, G.D., Shoyaib, M.: An adaptive gamma correction for image enhancement. EURASIP Journal on Image and Video Processing 2016
2016
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Lore, K.G., Akintayo, A., Sarkar, S.: Llnet: A deep autoencoder approach to natural low-light image enhancement. Pattern Recognition 61
2017
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Lv, F., Lu, F., Wu, J., Lim, C.: Mbllen: Low-light image/video enhancement using cnns. In: BMVC. vol. 220, p. 4. Northumbria University (2018)
2018
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2018
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Wang, R., Zhang, Q., Fu, C.W., Shen, X., Zheng, W.S., Jia, J.: Underexposed photo enhancement using deep illumination estimation. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. pp. 6849–6857 (2019)
2019
Cited alongside, same era.
Zhang, Y., Zhang, J., Guo, X.: Kindling the darkness: A practical low-light image enhancer. In: Proceedings of the 27th ACM international conference on multimedia. pp. 1632–1640 (2019)
2019
Cited alongside, same era.
Ma, L., Ma, T., Liu, R., Fan, X., Luo, Z.: Toward fast, flexible, and robust low-light image enhancement (2022)
2022
Later among the works it cites.
Mei, X., Ye, X., Zhang, X., Liu, Y., Wang, J., Hou, J., Wang, X.: Uir-net: A simple and effective baseline for underwater image restoration and enhancement. Remote Sensing 15
2022
Later among the works it cites.
2022
Later among the works it cites.
Wu, W., Weng, J., Zhang, P., Wang, X., Yang, W., Jiang, J.: 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. pp. 5901–5910 (2022)
2022
Later among the works it cites.
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2020
Cited alongside, same era.
Guo, C., Li, C., Guo, J., Loy, C.C., Hou, J., Kwong, S., Cong, R.: Zero-reference deep curve estimation for low-light image enhancement. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. pp. 1780–1789 (2020)
2020
Cited alongside, same era.
Moran, S., Marza, P., McDonagh, S., Parisot, S., Slabaugh, G.: Deeplpf: Deep local parametric filters for image enhancement. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. pp. 12826–12835 (2020)
2020
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Zamir, S.W., Arora, A., Khan, S., Hayat, M., Khan, F.S., Yang, M.H., Shao, L.: Learning enriched features for real image restoration and enhancement. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXV 16. pp. 492–511. Springer (2020)
2020
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Zhang, X., Chen, F., Wang, C., Tao, M., Jiang, G.P.: Sienet: Siamese expansion network for image extrapolation. IEEE Signal Processing Letters 27
2020
Cited alongside, same era.
2021
Cited alongside, same era.
Liu, R., Ma, L., Zhang, J., Fan, X., Luo, Z.: Retinex-inspired unrolling with cooperative prior architecture search for low-light image enhancement. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. pp. 10561–10570 (2021)
2021
Cited alongside, same era.
Pan, X., Zhan, X., Dai, B., Lin, D., Loy, C.C., Luo, P.: Exploiting deep generative prior for versatile image restoration and manipulation. IEEE Transactions on Pattern Analysis and Machine Intelligence 44
2021
Cited alongside, same era.
Xu, X., Wang, R., Fu, C.W., Jia, J.: Snr-aware low-light image enhancement. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. pp. 17714–17724 (2022)
2022
Later among the works it cites.
Cai, Y., Bian, H., Lin, J., Wang, H., Timofte, R., Zhang, Y.: Retinexformer: One-stage retinex-based transformer for low-light image enhancement. In: Proceedings of the IEEE/CVF International Conference on Computer Vision. pp. 12504–12513 (2023)
2023
Later among the works it cites.
Fu, Z., Yang, Y., Tu, X., Huang, Y., Ding, X., Ma, K.K.: Learning a simple low-light image enhancer from paired low-light instances. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. pp. 22252–22261 (2023)
2023
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2023
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Liang, Z., Li, C., Zhou, S., Feng, R., Loy, C.C.: Iterative prompt learning for unsupervised backlit image enhancement. In: Proceedings of the IEEE/CVF International Conference on Computer Vision. pp. 8094–8103 (2023)
2023
Later among the works it cites.
Yang, S., Ding, M., Wu, Y., Li, Z., Zhang, J.: Implicit neural representation for cooperative low-light image enhancement. In: Proceedings of the IEEE/CVF International Conference on Computer Vision. pp. 12918–12927 (2023)
2023
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2023
Later among the works it cites.
Zhang, X., Zhao, Y., Gu, C., Lu, C., Zhu, S.: Spa-former: An effective and lightweight transformer for image shadow removal. In: 2023 International Joint Conference on Neural Networks (IJCNN). pp. 1–8. IEEE (2023)
2023
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2023
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2024
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2024
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2024
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2024
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Zhang, X., Xu, Z., Tang, H., Gu, C., Chen, W., Zhu, S., Guan, X.: Enlighten-your-voice: When multimodal meets zero-shot low-light image enhancement (2024)
2024
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2024
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