  Limited-angle computed tomography (CT) is often used in clinical applications
such as C-arm CT for interventional imaging. However, CT images from limited
angles suffers from heavy artifacts due to incomplete projection data. Existing
iterative methods require extensive calculations but can not deliver
satisfactory results. Based on the observation that the artifacts from limited
angles have some directional property and are globally distributed, we propose
a novel multi-scale wavelet domain residual learning architecture, which
compensates for the artifacts. Experiments have shown that the proposed method
effectively eliminates artifacts, thereby preserving edge and global structures
of the image.
