Research into CUP-VISAR velocity reconstruction based on weighted DRUNet and total variation joint optimization

This Letter proposes a CUP-VISAR data reconstruction algorithm for laser-driven inertial confinement fusion (ICF) research. The algorithm combines weighted deep residual U-Net (DRUNet) and joint optimization with total variation (TV) to improve shockwave velocity fringe image reconstruc-tion. The simulation results demonstrate that the proposed algorithm outperforms the ADMM-TV and enhanced 3D total variation (E-3DTV) algorithms, enhancing the quality of the reconstructed images and thereby improving the accu-racy of velocity field calculations. Furthermore, it addresses the challenges of the high compression ratio caused by the diagnostic requirements of the larger number of sampling frames in the CUP-VISAR system and the issues of aliasing within a large encoding aperture. The proposed algorithm demonstrates good robustness to noise, ensuring reliable reconstruction even under Gaussian noise with a relative intensity of 0.05. This algorithm contributes to ICF diagnos-tics in complex environmental conditions and has theoretical significance and practical application value for achieving controlled thermonuclear fusion. (c) 2023 Optica Publishing Group