Li Shiqi, Li Hui, Li Tian, Su Chenbo, Wu Yuntao
Opt Lett. 2024 Oct 15;49(20):5866-5869. doi: 10.1364/OL.534069.
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.
多光谱成像在检测同色异谱材料方面具有巨大潜力。然而，传统的多光谱成像系统具有体积大、结构复杂和计算要求高的特点，限制了它们的实际应用。我们提出了一种联合优化的深度光学架构，该架构结合了液晶（LC）微透镜阵列（MLA）的特性和多级感知光谱重建网络（MLP-SRN）。该架构的核心是使用点扩散函数（PSF）光学卷积核将LC-MLA的物理特性集成到MLP-SRN中，解耦LC-MLA在不同电压下收集的光场特征信息。实验结果表明，纳入LC-MLA的物理特性不仅减小了系统尺寸和计算复杂度，而且在识别同色异谱材料方面表现出优异的性能。
