Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: SUPER Learning: A Supervised-Unsupervised Framework for Low-Dose CT
  Image Reconstruction   Recent years have witnessed growing interest in machine learning-based models
and techniques for low-dose X-ray CT (LDCT) imaging tasks. The methods can
typically be categorized into supervised learning methods and unsupervised or
model-based learning methods. Supervised learning methods have recently shown
success in image restoration tasks. However, they often rely on large training
sets. Model-based learning methods such as dictionary or transform learning do
not require large or paired training sets and often have good generalization
properties, since they learn general properties of CT image sets. Recent works
have shown the promising reconstruction performance of methods such as
PWLS-ULTRA that rely on clustering the underlying (reconstructed) image patches
into a learned union of transforms. In this paper, we propose a new
Supervised-UnsuPERvised (SUPER) reconstruction framework for LDCT image
reconstruction that combines the benefits of supervised learning methods and
(unsupervised) transform learning-based methods such as PWLS-ULTRA that involve
highly image-adaptive clustering. The SUPER model consists of several layers,
each of which includes a deep network learned in a supervised manner and an
unsupervised iterative method that involves image-adaptive components. The
SUPER reconstruction algorithms are learned in a greedy manner from training
data. The proposed SUPER learning methods dramatically outperform both the
constituent supervised learning-based networks and iterative algorithms for
LDCT, and use much fewer iterations in the iterative reconstruction modules.

23
The categories of this paper are: solv-int
34
The categories of this paper are: chem-ph
12
The categories of this paper are: nucl-th
33
The categories of this paper are: plasm-ph
2
The categories of this paper are: math
9
The categories of this paper are: nlin
31
The categories of this paper are: ao-sci
10
The categories of this paper are: q-bio
14
The categories of this paper are: math-ph
35
The categories of this paper are: mtrl-th
17
The categories of this paper are: q-fin
36
The categories of this paper are: cmp-lg
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The categories of this paper are: q-alg
27
The categories of this paper are: comp-gas
0
The categories of this paper are: hep-ph
11
The categories of this paper are: hep-lat