Reconstruction of spectral light field image based on compressed spectral imaging

This paper introduces a snapshot spectral volumetric imaging approach based on light field image slicing and encoding. By slicing and encoding light field information, followed by spectral dispersion and array reimaging lens acquisition of aliased data, a four-dimensional data hypercube is reconstructed using deep learning-based algorithms. This hypercube contains three-dimensional spatial information and one-dimensional spectral information of the scene. The proposed approach utilizes Sanpshot Compressed Imaging Mapping Spectrometer(SCIMS)principle for initial light field spectral data acquisition. Reconstruction of this data employs traditional algorithms like Alternating Direction Method of Multipliers (ADMM) and Generalized Alternating Projection (GAP), as well as deep learning methods such as LRSDN and PnP-DIP. Simulation experiments reveal that classical compressive sensing-based spectral data reconstruction algorithms perform poorly, especially affecting digital refocusing of individual spectral bands in light field images. In contrast, deep learning algorithms exhibit significant improvements, effectively extracting and preserving spatial distribution characteristics of light field data, thus robustly recovering light field information. This validates the effectiveness of the proposed spectral volumetric imaging approach and deep learning-based reconstruction methods. In future research, we will refine the mathematical model, integrate spatial and spectral correlations of light field imaging, develop specialized deep neural network algorithms, and enhance reconstruction of light field spectral data.