Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Equivariance Regularization for Image Reconstruction   In this work, we propose Regularization-by-Equivariance (REV), a novel
structure-adaptive regularization scheme for solving imaging inverse problems
under incomplete measurements. This regularization scheme utilizes the
equivariant structure in the physics of the measurements -- which is prevalent
in many inverse problems such as tomographic image reconstruction -- to
mitigate the ill-poseness of the inverse problem. Our proposed scheme can be
applied in a plug-and-play manner alongside with any classic first-order
optimization algorithm such as the accelerated gradient descent/FISTA for
simplicity and fast convergence. The numerical experiments in sparse-view X-ray
CT image reconstruction tasks demonstrate the effectiveness of our approach.

7
The categories of this paper are: hep-th
36
The categories of this paper are: cmp-lg
28
The categories of this paper are: alg-geom
10
The categories of this paper are: q-bio
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The categories of this paper are: nlin
3
The categories of this paper are: physics
16
The categories of this paper are: stat
33
The categories of this paper are: plasm-ph
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The categories of this paper are: gr-qc
24
The categories of this paper are: dg-ga
18
The categories of this paper are: econ
30
The categories of this paper are: q-alg
32
The categories of this paper are: atom-ph
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The categories of this paper are: quant-ph
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The categories of this paper are: adap-org
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The categories of this paper are: astro-ph