Web: http://arxiv.org/abs/2110.10093

Jan. 26, 2022, 2:10 a.m. | Junqi Tang, Subhadip Mukherjee, Carola-Bibiane Schönlieb

cs.CV updates on arXiv.org arxiv.org

We propose a new type of efficient deep-unrolling networks for solving
imaging inverse problems. Conventional deep-unrolling methods require full
forward operator and its adjoint across each layer, and hence can be
significantly more expensive computationally as compared with other end-to-end
methods that are based on post-processing of model-based reconstructions,
especially for 3D image reconstruction tasks. We develop a stochastic
(ordered-subsets) variant of the classical learned primal-dual (LPD), which is
a state-of-the-art unrolling network for tomographic image reconstruction. The
proposed learned …

arxiv deep stochastic

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