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This paper firstly proposes a convex bilevel optimization paradigm to formulate and optimize popular learning and vision problems in real-world scenarios.
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Y. Sun, B. Wohlberg, and U. S. Kamilov, “An online plug-and-play algorithm for regularized image reconstruction,” IEEE Transactions on Computational Imaging , vol. 5, no. 3, pp. 395–408, 2019
2019
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R. Wang, Q. Zhang, C.-W. Fu, X. Shen, W.-S. Zheng, and J. Jia, “Underexposed photo enhancement using deep illumination estimation,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2019, pp. 6849–6857
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Y. Sun, Z. Wu, X. Xu, B. Wohlberg, and U. S. Kamilov, “Scalable plug-and-play admm with convergence guarantees,” IEEE Transactions on Computational Imaging , vol. 7, pp. 849–863, 2021
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P. Nair, R. G. Gavaskar, and K. N. Chaudhury, “Fixed-point and objective convergence of plug-and-play algorithms,” IEEE Transactions on Computational Imaging , vol. 7, pp. 337–348, 2021
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R. G. Gavaskar, C. D. Athalye, and K. N. Chaudhury, “On plug-and-play regularization using linear denoisers,” IEEE Transactions on Image Processing , vol. 30, pp. 4802–4813, 2021
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