Photoelectric hybrid neural network based on ZnO nematic liquid crystal microlens array for hyperspectral imaging.

Abstract

The miniaturized imaging spectrometers face bottlenecks in reconstructing the high-resolution spectral image. In this study, we have proposed an optoelectronic hybrid neural network based on zinc oxide (ZnO) nematic liquid crystal (LC) microlens array (MLA). This architecture optimizes the parameters of the neural network by constructing the TV-L1-L2 objective function and using mean square error as a loss function, giving full play to the advantages of ZnO LC MLA. It adopts the ZnO LC-MLA as optical convolution to reduce the volume of the network. Experimental results show that the proposed architecture has reconstructed a 1536 × 1536 pixels resolution enhancement hyperspectral image in the wavelength range of [400 nm, 700 nm] in a relatively short time, and the spectral accuracy of reconstruction has reached just 1 nm.