  In the practical applications of computed tomography imaging, the projection
data may be acquired within a limited-angle range and corrupted by noises due
to the limitation of scanning conditions. The noisy incomplete projection data
results in the ill-posedness of the inverse problems. In this work, we
theoretically verify that the low-resolution reconstruction problem has better
numerical stability than the high-resolution problem. In what follows, a novel
low-resolution image prior based CT reconstruction model is proposed to make
use of the low-resolution image to improve the reconstruction quality. More
specifically, we build up a low-resolution reconstruction problem on the
down-sampled projection data, and use the reconstructed low-resolution image as
prior knowledge for the original limited-angle CT problem. We solve the
constrained minimization problem by the alternating direction method with all
subproblems approximated by the convolutional neural networks. Numerical
experiments demonstrate that our double-resolution network outperforms both the
variational method and popular learning-based reconstruction methods on noisy
limited-angle reconstruction problems.
