Multi-level noise reduction for 2D backlighting x-ray radiography using Frequency Residual U-Net

X-ray backlighting is a crucial diagnostic technique in inertial confinement fusion (ICF) experiments, enabling the observation of the geometrical structure of an imploding capsule. However, x-ray emission from a short-pulse laser illuminating a metallic strip often proves insufficient and unstable, resulting in high noise levels in observed images that complicate data analysis. In this study, we propose a comprehensive framework for multi-level noise reduction in x-ray backlighting images, integrating data synthetic techniques with a novel multi-level noise removal method based on Frequency Residual U-Net (FR-UNet). The observed x-ray images are decomposed into distinct signal and noise components. The signal is simulated based on the underlying backlighting geometry, incorporating a range of relevant parameters to accurately model the setup variance. The noise model is constructed through a comprehensive analysis of both shot noise and periodic speckle noise. The FR-UNet model is trained on synthetically generated images and subsequently applied to experimental data. Our framework demonstrates a significant improvement in image quality, with the signal-to-noise ratio increasing from ∼ 10 -20 dB to ∼ 30 dB. Further evaluation on an open dataset shows favorable results, achieving a peak signal-to-noise ratio of ∼ 35 and a structural similarity index measure of ∼ 0.96 . This approach offers substantial potential for enhancing image quality and diagnostic accuracy in inertial confinement fusion (ICF) experiments and can be adapted to similar applications involving x-ray backlighting systems in other high-energy physics contexts.