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Neural network denoising of x-ray images from high-energy-density experiments

Journal Article · · Review of Scientific Instruments
DOI:https://doi.org/10.1063/5.0207005· OSTI ID:2406543
 [1];  [1];  [1];  [1];  [1]
  1. Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)
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Noise is a consistent problem for x-ray transmission images of High-Energy-Density (HED) experiments because it can significantly affect the accuracy of inferring quantitative physical properties from these images. We consider experiments that use x-ray area backlighting to image a thin layer of opaque material within a physics package to observe its hydrodynamic evolution. The spatial variance of the x-ray transmission across the system due to changing opacity serves as an analog for measuring density in this evolving layer. The noise in these images adds nonphysical variations in measured intensity, which can significantly reduce the accuracy of our inferred densities, particularly at small spatial scales. Denoising these images is thus necessary to improve our quantitative analysis, but any denoising method also affects the underlying information in the image. In this paper, we present a method for denoising HED x-ray images via a deep convolutional neural network model with a modified DenseNet architecture. In our denoising framework, we estimate the noise present in the real (data) images of interest and apply the inferred noise distribution to a set of natural images. These synthetic noisy images are then used to train a neural network model to recognize and remove noise of that character. We show that our trained denoiser network significantly reduces the noise in our experimental images while retaining important physical features.
Research Organization:
Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)
Sponsoring Organization:
USDOE National Nuclear Security Administration (NNSA)
Grant/Contract Number:
89233218CNA000001
OSTI ID:
2406543
Report Number(s):
LA-UR--23-22491
Journal Information:
Review of Scientific Instruments, Journal Name: Review of Scientific Instruments Journal Issue: 6 Vol. 95; ISSN 0034-6748
Publisher:
American Institute of Physics (AIP)Copyright Statement
Country of Publication:
United States
Language:
English

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Figures / Tables (11)

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Related Subjects

47 OTHER INSTRUMENTATION
Artificial neural networks
Convolutional neural network
Hydrodynamics experiments
Inertial confinement usion
ICF
High-energy-density physics
NIF
Denoising
Neural networks
Machines learning
Optical imaging