Feature fusion and variational autoencoder based deep coded aperture design for a CUP-VISAR diagnostic system
Year: 2025
Type: article
Abstract: In this Letter, a coding aperture design framework is introduced for data sampling of a CUP-VISAR system in laser inertial confinement fusion (ICF) research.It enhances shock wave velocity fringe reconstruction through feature fusion with a convolutional variational auto-encoder (CVAE) network.Simulation and experimental results indicate that, compared to random coding aperture, the proposed coding matrices exhibit superior reconstruction quality, achieving more accurate fringe pattern reconstruction and resolving coding information aliasing.In the experiments, the system signal-to-noise ratio (SNR) and reconstruction quality can be improved by increasing the light transmittance of the encoding matrix.This framework aids in diagnosing ICF in challenging experimental settings.
Source: Chinese Optics Letters
Authors: Miao Li, Chenyan Wang, Xi Wang, Lingqiang Zhang, Chaorui Chen, Zhaohui Guo, Xueyin Zhao
Institution: Chongqing University of Posts and Telecommunications
Language: English
Subfield: Nuclear and High Energy Physics
Field: Physics and Astronomy
Domain: Physical Sciences
Open Access status: bronze
Awards: G305060397, G4646619473, G5914454059
OpenAlex ID: W4409372300