Open AccessDirect generation of starting points for freeform off-axis three-mirror imaging system design using neural network based deep-learning
Author(s) -
Tong Yang,
Dewen Cheng,
Yongtian Wang
Publication year - 2019
Publication title -
optics express
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.394
H-Index - 271
ISSN - 1094-4087
DOI - 10.1364/oe.27.017228
Subject(s) - computer science , artificial neural network , deep learning , set (abstract data type) , process (computing) , artificial intelligence , optics , field (mathematics) , computer vision , physics , programming language , operating system , mathematics , pure mathematics
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.