Reconstruction of spectral light field image based on compressed spectral imaging

Wanting Dai; Xiaoming Ding; Yazhou Feng; Chuanwang Zhang; Hao Yuan; Qiangqiang Yan

doi:10.1117/12.3045867

13 December 2024 Reconstruction of spectral light field image based on compressed spectral imaging

Wanting Dai, Xiaoming Ding, Yazhou Feng, Chuanwang Zhang, Hao Yuan, Qiangqiang Yan

Proceedings Volume 13501, AOPC 2024: Computational Imaging Technology; 1350102 (2024) https://doi.org/10.1117/12.3045867
Event: Applied Optics and Photonics China 2024 (AOPC2024), 2024, Beijing, China

Abstract

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.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Wanting Dai, Xiaoming Ding, Yazhou Feng, Chuanwang Zhang, Hao Yuan, and Qiangqiang Yan "Reconstruction of spectral light field image based on compressed spectral imaging", Proc. SPIE 13501, AOPC 2024: Computational Imaging Technology, 1350102 (13 December 2024); https://doi.org/10.1117/12.3045867

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KEYWORDS

Reconstruction algorithms

Image restoration

Deep learning

Image compression

Imaging spectroscopy

Imaging systems

Cameras

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