Provably convergent learned inexact descent algorithm for low-dose CT reconstruction. (English) Zbl 07915984
Summary: We propose an Efficient Inexact Learned Descent-type Algorithm (ELDA) for a class of nonconvex and nonsmooth variational models, where the regularization consists of a sparsity enhancing term and non-local smoothing term for learned features. The ELDA improves the performance of the LDA in Chen et al. (SIAM J Imag Sci 14(4), 1532–1564, 2021) by reducing the number of the subproblems from two to one for most of the iterations and allowing inexact gradient computation. We generate a deep neural network, whose architecture follows the algorithm exactly for low-dose CT (LDCT) reconstruction. The network inherits the convergence behavior of the algorithm and is interpretable as a solution of the varational model and parameter efficient. The experimental results from the ablation study and comparisons with several state-of-the-art deep learning approaches indicate the promising performance of the proposed method in solution accuracy and parameter efficiency.
MSC:
| 90C26 | Nonconvex programming, global optimization |
| 65K05 | Numerical mathematical programming methods |
| 65K10 | Numerical optimization and variational techniques |
| 68U10 | Computing methodologies for image processing |
Keywords:
learned inexact descent algorithm; nonconvex nonsmooth optimization; deep learning; image reconstructionSoftware:
PyTorchReferences:
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