Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Low Dose CT Denoising via Joint Bilateral Filtering and Intelligent
  Parameter Optimization   Denoising of clinical CT images is an active area for deep learning research.
Current clinically approved methods use iterative reconstruction methods to
reduce the noise in CT images. Iterative reconstruction techniques require
multiple forward and backward projections, which are time-consuming and
computationally expensive. Recently, deep learning methods have been
successfully used to denoise CT images. However, conventional deep learning
methods suffer from the 'black box' problem. They have low accountability,
which is necessary for use in clinical imaging situations. In this paper, we
use a Joint Bilateral Filter (JBF) to denoise our CT images. The guidance image
of the JBF is estimated using a deep residual convolutional neural network
(CNN). The range smoothing and spatial smoothing parameters of the JBF are
tuned by a deep reinforcement learning task. Our actor first chooses a
parameter, and subsequently chooses an action to tune the value of the
parameter. A reward network is designed to direct the reinforcement learning
task. Our denoising method demonstrates good denoising performance, while
retaining structural information. Our method significantly outperforms state of
the art deep neural networks. Moreover, our method has only two parameters,
which makes it significantly more interpretable and reduces the 'black box'
problem. We experimentally measure the impact of our intelligent parameter
optimization and our reward network. Our studies show that our current setup
yields the best results in terms of structural preservation.

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The categories of this paper are: q-alg
37
The categories of this paper are: supr-con
27
The categories of this paper are: comp-gas
20
The categories of this paper are: acc-phys
3
The categories of this paper are: physics
17
The categories of this paper are: q-fin
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The categories of this paper are: chem-ph
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The categories of this paper are: mtrl-th
9
The categories of this paper are: nlin
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The categories of this paper are: adap-org
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The categories of this paper are: ao-sci
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The categories of this paper are: stat
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The categories of this paper are: bayes-an
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The categories of this paper are: nucl-th
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The categories of this paper are: hep-ex
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The categories of this paper are: hep-th