Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Two-layer Residual Sparsifying Transform Learning for Image
  Reconstruction   Signal models based on sparsity, low-rank and other properties have been
exploited for image reconstruction from limited and corrupted data in medical
imaging and other computational imaging applications. In particular,
sparsifying transform models have shown promise in various applications, and
offer numerous advantages such as efficiencies in sparse coding and learning.
This work investigates pre-learning a two-layer extension of the transform
model for image reconstruction, wherein the transform domain or filtering
residuals of the image are further sparsified in the second layer. The proposed
block coordinate descent optimization algorithms involve highly efficient
updates. Preliminary numerical experiments demonstrate the usefulness of a
two-layer model over the previous related schemes for CT image reconstruction
from low-dose measurements.

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The categories of this paper are: plasm-ph
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The categories of this paper are: bayes-an
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The categories of this paper are: hep-th
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The categories of this paper are: atom-ph
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The categories of this paper are: mtrl-th
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The categories of this paper are: cmp-lg
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The categories of this paper are: math-ph
3
The categories of this paper are: physics
0
The categories of this paper are: hep-ph
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The categories of this paper are: q-alg
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The categories of this paper are: hep-lat
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The categories of this paper are: nlin
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The categories of this paper are: hep-ex
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The categories of this paper are: comp-gas
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The categories of this paper are: q-fin
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The categories of this paper are: chem-ph