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Award ID contains: 1925263

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  1. ; (, Journal of Computational Physics)
    Free, publicly-accessible full text available March 1, 2026
  2. ; ; ; ; (, Journal of Computational Physics)
    Free, publicly-accessible full text available March 1, 2026
  3. ; ; ; (, SIAM Journal on Numerical Analysis)
    Free, publicly-accessible full text available February 28, 2026
  4. ; ; (, SIAM Journal on Scientific Computing)
    Full Text Available 
  5. ; ; ; (, IEEE Conference on Decision and Control)
    Full Text Available 
  6. ; ; ; ; (, International Conference on Medical Image Computing and Computer Assisted Intervention)
    Accepted Manuscript Full Text Available
  7. ; ; (, 2022 IEEE 61st Conference on Decision and Control (CDC))
  8. ; ; ; (, Medical Image Computing and Computer Assisted Intervention – MICCAI 2022)
    Wang, L. (Ed.)
    Accepted Manuscript Full Text Available
  9. ; ; ; (, Springer)
    Full Text Available 
  10. ; ; (, Journal of Imaging)
    Learned optimization algorithms are promising approaches to inverse problems by leveraging advanced numerical optimization schemes and deep neural network techniques in machine learning. In this paper, we propose a novel deep neural network architecture imitating an extra proximal gradient algorithm to solve a general class of inverse problems with a focus on applications in image reconstruction. The proposed network features learned regularization that incorporates adaptive sparsification mappings, robust shrinkage selections, and nonlocal operators to improve solution quality. Numerical results demonstrate the improved efficiency and accuracy of the proposed network over several state-of-the-art methods on a variety of test problems. 
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