Enhanced Neural Architecture for Real-Time Deep Learning Wavefront Sensing
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 (CNN). We introduce a novel multi-objective neural...
Saved in:
Main Authors: | Jianyi Li, Qingfeng Liu, Liying Tan, Jing Ma, Nanxing Chen |
---|---|
Format: | Article |
Language: | English |
Published: |
MDPI AG
2025-01-01
|
Series: | Sensors |
Subjects: | |
Online Access: | https://www.mdpi.com/1424-8220/25/2/480 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Design considerations for wavefront sensing with self-referencing interferometers in adaptive optics systems
by: MacGillivray Alexander C., et al.
Published: (2025-01-01) -
Real-time adaptive optics for high-power laser beam correction in the strong turbulence
by: A.L. Rukosuev, et al.
Published: (2024-08-01) -
Wavefront Correction for Extended Sources Imaging Based on a 97-Element MEMS Deformable Mirror
by: Huizhen Yang, et al.
Published: (2024-12-01) -
Study of Point Scanning Detection Mechanisms for Vibration Signals with Wavefront Sensors
by: Quan Luo, et al.
Published: (2025-01-01) -
OpenWFS—a library for conducting and simulating wavefront shaping experiments
by: Jeroen H Doornbos, et al.
Published: (2025-01-01)