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We consider the variational reconstruction framework for inverse problems and propose to learn a data-adaptive input-convex neural network (ICNN) as the regularization functional.
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K. H. Jin, M. T. McCann, E. Froustey, and M. Unser, “Deep convolutional neural network for inverse problems in imaging,” IEEE Transactions on Image Processing , vol. 26, no. 9, pp. 4509–4522, 2017
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B. Amos, L. Xu, and J. Z. Kolter, “Input convex neural networks,” in International Conference on Machine Learning , 2017, pp. 146–155
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J. Sulam, V. Papyan, Y. Romano, and M. Elad, “Multilayer convolutional sparse modeling: pursuit and dictionary learning,” IEEE Transactions on Signal Processing , vol. 5, no. 15, pp. 4090–4104, 2018
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E. Kobler, A. Effland, K. Kunisch, and T. Pock, “Total deep variation for linear inverse problems,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2020, pp. 7549–7558
2020
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2020
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