Breaking Boundaries: A Universal Wavefront Reconstruction Approach for High-resolution Solar Imaging

Abstract

This Letter proposes a universal wavefront reconstruction approach based on a coupled data set and neural network, aiming to overcome the limitations of current algorithms in terms of universality and wavefront sensing accuracy for variable imaging objects. First, a novel data set, Multi-Object Wavefront Coupling Dataset (MOCD-Dataset), is developed to provide diverse data and enable the network to learn universal wavefront features. Next, a new universal wavefront reconstruction network called Object-Independent Wavefront Decoupling Network (OIWD-Net) is introduced, aiming to separate imaging object information from multiple variable images. Our algorithm eliminates the need for specialized wavefront sensors, has a simple system, high light energy utilization, and does not require customized models for each different type of imaging objects, making it highly practical. By combining the MOCD-Dataset and the OIWD-Net, excellent accuracy in wavefront reconstruction of different imaging objects has been achieved. This research provides a new solution for high-resolution image restoration in fields such as solar structure observation and astronomical high-resolution imaging.