Journal Article

Attentionpd-resunet: a laser phase retrieval network based on attention phase diversity

Li, Chen, Jing, Wenbo, Zhao, Haili, Di, Zilong, Bai, Haoyang, Song, Mingzhe, Meng, JiaHe

2026

Overview

Laser phase retrieval plays a crucial role in the evaluation of laser beam quality, yet model-based approaches are often limited by their sensitivity to initial conditions and susceptibility to local minima. To address these challenges, we propose AttentionPD-ResUNet, a phase retrieval framework that integrates the Phase Diversity (PD) method with attention mechanisms. Specifically, focused and defocused intensity images acquired via PD are employed as inputs to the network, which incorporates SE channel recalibration, ASPP-based multi-scale sampling, and spatial attention modules. This design enables the establishment of an end-to-end nonlinear mapping from the measured intensity distributions to the underlying wavefront phase. In comparative experiments, the proposed method achieves an RMSE of 0.068 and an MAE of 0.041 relative to the ground truth, with an average inference time of 0.41 s, thereby presenting a promising approach for reliable laser beam quality assessment.

• Publisher: Springer Berlin Heidelberg,Springer Nature B.V
• Subject: Datasets / Deep learning / Engineering / Image acquisition / Initial conditions / Laser beams / Lasers / Methods / Optical Devices / Optics / Phase diversity / Phase retrieval / Photonics / Physical Chemistry / Physics / Physics and Astronomy / Propagation / Quality assessment / Quantum Optics / Simulation / Space telescopes / Wave fronts