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

Abstract

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.