Compressive sensing (CS) has proved effective for tomographic reconstruction from
sparsely collected data or under-sampled measurements, which are practically important for
few-view computed tomography (CT), tomosynthesis, interior tomography, and so on. To
perform sparse-data CT, the iterative reconstruction commonly uses regularizers in the CS
framework. Currently, how to choose the parameters adaptively for regularization is a major
open problem. In this paper, inspired by the idea of machine learning especially deep …

Compressive sensing (CS) has proved effective for tomographic reconstruction from
sparsely collected data or under-sampled measurements, which are practically important for
few-view CT, tomosynthesis, interior tomography, and so on. To perform sparse-data CT, the
iterative reconstruction commonly use regularizers in the CS framework. Currently, how to
choose the parameters adaptively for regularization is a major open problem. In this paper,
inspired by the idea of machine learning especially deep learning, we unfold a state-of-the …