Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Ultra Low-Parameter Denoising: Trainable Bilateral Filter Layers in
  Computed Tomography   Computed tomography is widely used as an imaging tool to visualize
three-dimensional structures with expressive bone-soft tissue contrast.
However, CT resolution and radiation dose are tightly entangled, highlighting
the importance of low-dose CT combined with sophisticated denoising algorithms.
Most data-driven denoising techniques are based on deep neural networks and,
therefore, contain hundreds of thousands of trainable parameters, making them
incomprehensible and prone to prediction failures. Developing understandable
and robust denoising algorithms achieving state-of-the-art performance helps to
minimize radiation dose while maintaining data integrity. This work presents an
open-source CT denoising framework based on the idea of bilateral filtering. We
propose a bilateral filter that can be incorporated into a deep learning
pipeline and optimized in a purely data-driven way by calculating the gradient
flow toward its hyperparameters and its input. Denoising in pure image-to-image
pipelines and across different domains such as raw detector data and
reconstructed volume, using a differentiable backprojection layer, is
demonstrated. Although only using three spatial parameters and one range
parameter per filter layer, the proposed denoising pipelines can compete with
deep state-of-the-art denoising architectures with several hundred thousand
parameters. Competitive denoising performance is achieved on x-ray microscope
bone data (0.7053 and 33.10) and the 2016 Low Dose CT Grand Challenge dataset
(0.9674 and 43.07) in terms of SSIM and PSNR. Due to the extremely low number
of trainable parameters with well-defined effect, prediction reliance and data
integrity is guaranteed at any time in the proposed pipelines, in contrast to
most other deep learning-based denoising architectures.

12
The categories of this paper are: nucl-th
28
The categories of this paper are: alg-geom
35
The categories of this paper are: mtrl-th
16
The categories of this paper are: stat
21
The categories of this paper are: adap-org
2
The categories of this paper are: math
27
The categories of this paper are: comp-gas
17
The categories of this paper are: q-fin
6
The categories of this paper are: astro-ph
14
The categories of this paper are: math-ph
10
The categories of this paper are: q-bio
33
The categories of this paper are: plasm-ph
20
The categories of this paper are: acc-phys
15
The categories of this paper are: nucl-ex
0
The categories of this paper are: hep-ph
4
The categories of this paper are: cond-mat