Practical snapshot hyperspectral imaging with DOE. (September 2022)

• Record Type: Journal Article
• Authors: Hu, Haiquan; Zhou, Hao; Xu, Zhihai; Li, Qi; Feng, Huajun; Chen, Yueting; Jiang, Tingting; Xu, Wenbin
• Is Part Of: Optics and lasers in engineering. Volume 156(2022)
• Publication Date: 2022-09
• Subjects: Hyperspectral imaging -- Diffractive optical element -- Iterative algorithm -- Neural network -- Snapshot; Lasers in engineering -- Periodicals; Optical measurements -- Periodicals; Optics -- Periodicals; Lasers en ingénierie -- Périodiques; Mesures optiques -- Périodiques; Optique -- Périodiques; 621.36605
• DOI: 10.1016/j.optlaseng.2022.107098
• Languages: English
• ISSNs: 0143-8166

Abstract

Highlights: We propose a practical hyperspectral camera with a designed diffractive optical element (DOE). The designed DOE is more practical to capture natural scenes and better to reconstruct hyperspectral images. Using the proposed kernel error and dual-path U-net network to optimize the network structure, we improve the interpretability of the network and achieve better reconstructed results. Abstract: As the pursuit of snapshot spectral imaging continued to grow, traditional hyperspectral imaging systems have been too enormous and too slow to implement in real scenarios. Considering the portability and the demand for snapshots, in this study, we proposed a practical hyperspectral camera with a designed diffractive optical element (DOE). The designed DOE distinguished the incident spectrums and converged them into different point spread functions (PSFs) on the imaging plane. Utilizing the spectrally-varying PSF information, we engaged the iterative algorithm with the deep-learning model to reconstruct hyperspectral data. Experimental results demonstrated that the proposed system performed at least as well as the current methods and could achieve great spatial resolution and spectral accuracy in spectral imaging. This proposed system had good potential in portable hyperspectral imaging system.