Unrolled three-operator splitting for parameter-map learning in Low Dose X-ray CT reconstruction
Year: 2023
Type: preprint
Abstract: We propose a method for fast and automatic estimation of spatially dependent regularization maps for total variation-based (TV) tomography reconstruction. The estimation is based on two distinct sub-networks, with the first sub-network estimating the regularization parameter-map from the input data while the second one unrolling T iterations of the Primal-Dual Three-Operator Splitting (PD3O) algorithm. The latter approximately solves the corresponding TV-minimization problem incorporating the previously estimated regularization parameter-map. The overall network is then trained end-to-end in a supervised learning fashion using pairs of clean-corrupted data but crucially without the need of having access to labels for the optimal regularization parameter-maps.
Source: arXiv (Cornell University)
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
Awards: G1094694140, G1372115314, G1952997424, G2033548659, Tomographic Imaging: UK Collaborative Computational Projects, A Reconstruction Toolkit for Multichannel CT, CCP in Synergistic Reconstruction for Biomedical Imaging, G5853193109, G7422891701, EXC 2046: MATH+: Berlin Mathematics Research Center, Tomographic Imaging, G8964763783
OpenAlex ID: W4366328012