Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Improving Limited Angle CT Reconstruction with a Robust GAN Prior   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.

33
The categories of this paper are: plasm-ph
34
The categories of this paper are: chem-ph
27
The categories of this paper are: comp-gas
11
The categories of this paper are: hep-lat
37
The categories of this paper are: supr-con
36
The categories of this paper are: cmp-lg
20
The categories of this paper are: acc-phys
10
The categories of this paper are: q-bio
28
The categories of this paper are: alg-geom
13
The categories of this paper are: quant-ph
32
The categories of this paper are: atom-ph
17
The categories of this paper are: q-fin
8
The categories of this paper are: hep-ex
0
The categories of this paper are: hep-ph
31
The categories of this paper are: ao-sci
9
The categories of this paper are: nlin