Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Convolutional Analysis Operator Learning by End-To-End Training of
  Iterative Neural Networks   The concept of sparsity has been extensively applied for regularization in
image reconstruction. Typically, sparsifying transforms are either pre-trained
on ground-truth images or adaptively trained during the reconstruction.
Thereby, learning algorithms are designed to minimize some target function
which encodes the desired properties of the transform. However, this procedure
ignores the subsequently employed reconstruction algorithm as well as the
physical model which is responsible for the image formation process. Iterative
neural networks - which contain the physical model - can overcome these issues.
In this work, we demonstrate how convolutional sparsifying filters can be
efficiently learned by end-to-end training of iterative neural networks. We
evaluated our approach on a non-Cartesian 2D cardiac cine MRI example and show
that the obtained filters are better suitable for the corresponding
reconstruction algorithm than the ones obtained by decoupled pre-training.

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The categories of this paper are: math-ph
2
The categories of this paper are: math
21
The categories of this paper are: adap-org
35
The categories of this paper are: mtrl-th
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The categories of this paper are: supr-con
24
The categories of this paper are: dg-ga
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The categories of this paper are: plasm-ph
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The categories of this paper are: ao-sci
5
The categories of this paper are: gr-qc
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The categories of this paper are: chao-dyn
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The categories of this paper are: bayes-an
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The categories of this paper are: alg-geom
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The categories of this paper are: quant-ph
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The categories of this paper are: hep-lat
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The categories of this paper are: patt-sol
6
The categories of this paper are: astro-ph