To achieve real-time deep learning wavefront sensing (DLWFS) of dynamic random wavefront distortions induced by atmospheric turbulence, this study proposes an enhanced wavefront sensing neural network (WFSNet) based on convolutional neural networks (...
In this paper, research was conducted on Deep Learning Wavefront Sensing (DLWS) neural networks using simulated atmospheric turbulence datasets, and a novel DLWS was proposed based on attention mechanisms and Convolutional Neural Networks (CNNs). The...
At present, most wavefront sensing methods analyze the wavefront aberration from light intensity images taken in dark environments. However, in general conditions, these methods are limited due to the interference of various external light sources. I...
Phase diversity wavefront sensing (PDWS) is a model-based wavefront estimation technique that avoids additional optical components, making it suitable for resource-constrained environments. However, conventional optimization-based PDWS methods often...
Atmospheric turbulence significantly degrades the performance of High Energy Laser (HEL) systems by distorting the laser wavefront as it propagates through the atmosphere. Conventional correction techniques rely on Adaptive Optics (AO), which preserv...
The Shack–Hartmann wavefront sensor (SHWFS) is widely utilized for ocular aberration measurement. However, large ocular aberrations caused by individual differences can easily make the spot move out of the range of the corresponding sub-apertur...
A segmented primary mirror is very important for extra-large astronomical telescopes, in order to detect the phase error between segmented mirrors. Traditional iterative algorithms are hard to detect co−phasing aberrations in real time due to t...
The fast and accurate reconstruction of the turbulence phase is crucial for compensating atmospheric disturbances in free-space coherent optical communication. Traditional methods suffer from slow convergence and inadequate phase reconstruction accur...
Wavefront sensing is an essential technique in optical imaging, adaptive optics, and atmospheric turbulence correction. Traditional wavefront reconstruction methods, including the Gerchberg–Saxton (GS) algorithm and phase diversity (PD) techniq...
Phase retrieval from supervised learning neural networks is restricted due to the problem of obtaining labels. To address this situation, in the present paper, we propose a phase retrieval model of self-supervised physical deep learning combined with...