Direct generation of starting points for freeform off-axis three-mirror imaging system design using neural network based deep-learning

In this paper, we propose a framework of starting points generation for freeform reflective triplet using back-propagation neural network based deep-learning. The network is trained using various system specifications and the corresponding surface data obtained by system evolution as the data set. G...

Full description

Saved in:
Bibliographic Details
Published in:Optics express 2019-06, Vol.27 (12), p.17228-17238
Main Authors: Yang, Tong, Cheng, Dewen, Wang, Yongtian
Format: Article
Language:English
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:In this paper, we propose a framework of starting points generation for freeform reflective triplet using back-propagation neural network based deep-learning. The network is trained using various system specifications and the corresponding surface data obtained by system evolution as the data set. Good starting points of specific system specifications for further optimization can be generated immediately using the obtained network in general. The feasibility of this design process is validated by designing the Wetherell-configuration freeform off-axis reflective triplet. The amount of time and human effort as well as the dependence on advanced design skills are significantly reduced. These results highlight the powerful ability of deep learning in the field of freeform imaging optical design.
ISSN:1094-4087
1094-4087
DOI:10.1364/OE.27.017228