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Abstract #3382

Optimal Regularization Parameter Selection for Constrained Reconstruction Using Deep Learning

Xi Peng1,2, Fan Lam1, Yudu Li1,3, Bryan Clifford1,3, Brad Sutton1,4, and Zhi-Pei Liang1,3

1Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, IL, United States, 2Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Shenzhen, China, 3Department of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign, Urbana, IL, United States, 4Department of Bioengineering, University of Illinois at Urbana-Champaign, Urbana, IL, United States

Regularization is widely used for solving ill-posed image reconstruction problems and an appropriate selection of the regularization parameter is critical in ensuring high-quality reconstructions. While many methods have been proposed to address this problem, selecting a regularization parameter for optimal performance (under a specific metric) in a computationally efficient manner is still an open problem. We propose here a novel deep learning based method for regularization parameter selection. Specifically, a convolutional neural network is designed to predict the optimal parameter from an “arbitrary” initial parameter choice. The proposed method has been evaluated using experimental data, demonstrating its capability to learn the optimal parameter for two different L1-regularized reconstruction problems.

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