Shot Noise Reduction in Radiographic and Tomographic Multi-Channel
Imaging with Self-Supervised Deep Learning
- URL: http://arxiv.org/abs/2303.14429v1
- Date: Sat, 25 Mar 2023 10:33:41 GMT
- Title: Shot Noise Reduction in Radiographic and Tomographic Multi-Channel
Imaging with Self-Supervised Deep Learning
- Authors: Yaroslav Zharov, Evelina Ametova, Rebecca Spiecker, Tilo Baumbach,
Genoveva Burca, Vincent Heuveline
- Abstract summary: Noise is an important issue for radiographic and tomographic imaging techniques.
It becomes particularly critical in applications where additional constraints force a strong reduction of the Signal-to-Noise Ratio (SNR) per image.
We report on a method for improving the quality of noisy multi-channel (time or energy-resolved) imaging datasets.
- Score: 0.9786690381850356
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Noise is an important issue for radiographic and tomographic imaging
techniques. It becomes particularly critical in applications where additional
constraints force a strong reduction of the Signal-to-Noise Ratio (SNR) per
image. These constraints may result from limitations on the maximum available
flux or permissible dose and the associated restriction on exposure time.
Often, a high SNR per image is traded for the ability to distribute a given
total exposure capacity per pixel over multiple channels, thus obtaining
additional information about the object by the same total exposure time. These
can be energy channels in the case of spectroscopic imaging or time channels in
the case of time-resolved imaging. In this paper, we report on a method for
improving the quality of noisy multi-channel (time or energy-resolved) imaging
datasets. The method relies on the recent Noise2Noise (N2N) self-supervised
denoising approach that learns to predict a noise-free signal without access to
noise-free data. N2N in turn requires drawing pairs of samples from a data
distribution sharing identical signals while being exposed to different samples
of random noise. The method is applicable if adjacent channels share enough
information to provide images with similar enough information but independent
noise. We demonstrate several representative case studies, namely spectroscopic
(k-edge) X-ray tomography, in vivo X-ray cine-radiography, and
energy-dispersive (Bragg edge) neutron tomography. In all cases, the N2N method
shows dramatic improvement and outperforms conventional denoising methods. For
such imaging techniques, the method can therefore significantly improve image
quality, or maintain image quality with further reduced exposure time per
image.
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