Learned liquid crystal microlens array for joint optimized deep optical architecture in identifying metameric materials

Multispectral imaging holds great promise for the detection of metameric materials. However, traditional multispectral imaging systems are characterized by their large volume, complex structure, and high computational requirements, limiting their practical application. We propose a jointly optimized...

Full description

Saved in:
Bibliographic Details
Published in:Optics letters 2024-10, Vol.49 (20), p.5866
Main Authors: Li, Shiqi, Li, Hui, Li, Tian, Su, Chenbo, Wu, Yuntao
Format: Article
Language:English
Subjects:
Arrays
Complexity
Decoupling
Liquid crystals
Microlenses
Optical properties
Physical properties
Point spread functions
Citations: Items that this one cites
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Multispectral imaging holds great promise for the detection of metameric materials. However, traditional multispectral imaging systems are characterized by their large volume, complex structure, and high computational requirements, limiting their practical application. We propose a jointly optimized deep optical architecture that combines the liquid crystal (LC) microlens array (MLA) characteristics and a multi-level perceptual spectral reconstruction network (MLP-SRN). The core of the architecture is to integrate the physical properties of the LC-MLA into the MLP-SRN using point spread function (PSF) optical convolution kernels, decoupling the light-field characteristic information collected by the LC-MLA at different voltages. Experimental results demonstrate that the incorporation of the physical properties of the LC-MLA not only reduces the system size and computational complexity but demonstrates excellent performance in identifying a metameric material.
ISSN:0146-9592
1539-4794
1539-4794
DOI:10.1364/OL.534069