  We propose a provably convergent method, called Efficient Learned Descent
Algorithm (ELDA), for low-dose CT (LDCT) reconstruction. ELDA is a highly
interpretable neural network architecture with learned parameters and meanwhile
retains convergence guarantee as classical optimization algorithms. To improve
reconstruction quality, the proposed ELDA also employs a new non-local feature
mapping and an associated regularizer. We compare ELDA with several
state-of-the-art deep image methods, such as RED-CNN and Learned Primal-Dual,
on a set of LDCT reconstruction problems. Numerical experiments demonstrate
improvement of reconstruction quality using ELDA with merely 19 layers,
suggesting the promising performance of ELDA in solution accuracy and parameter
efficiency.

Image and Video Processing;Computer Vision and Pattern Recognition