  Limited angle CT reconstruction is an under-determined linear inverse problem
that requires appropriate regularization techniques to be solved. In this work
we study how pre-trained generative adversarial networks (GANs) can be used to
clean noisy, highly artifact laden reconstructions from conventional
techniques, by effectively projecting onto the inferred image manifold. In
particular, we use a robust version of the popularly used GAN prior for inverse
problems, based on a recent technique called corruption mimicking, that
significantly improves the reconstruction quality. The proposed approach
operates in the image space directly, as a result of which it does not need to
be trained or require access to the measurement model, is scanner agnostic, and
can work over a wide range of sensing scenarios.
