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Learning based compressive snapshot spectral light field imaging with RGB sensors.

Tianyu He, Wenyi Ren, Yang Feng

|October 20, 2023

Summary

This study introduces a low-cost method for spectral light field (SLF) imaging using RGB cameras. The technique reconstructs 4D SLF data, enabling detailed spatial-spectral-angular information capture.

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Area of Science:

Optics and Photonics
Computer Vision
Computational Imaging

Background:

Multidimensional optical sensing, including spectral light field (SLF) imaging, is crucial for fields like computer vision and microscopy.
Traditional SLF sensors often require expensive components such as beam-splitters or multiple cameras.
There is a need for cost-effective SLF imaging solutions.

Purpose of the Study:

To develop a low-cost method for capturing four-dimensional (4D) spectral light field data.
To reconstruct SLF information using readily available RGB light field cameras.
To provide an accessible alternative for advanced optical sensing.

Main Methods:

A compressed snapshot imaging method based on RGB light field cameras.
Utilizing a U-shaped neural network incorporating multi-head self-attention and unparameterized Fourier transform modules.
Leveraging the principles of compressive sensing for data reconstruction.

Main Results:

Successful reconstruction of 4D SLF data from RGB light field camera measurements.
Achieved spectral resolution of 10 nm within the 400-700 nm range.
Obtained angular resolution of 9x9 and spatial resolution of 622x432.

Conclusions:

The proposed method offers a low-cost approach to SLF imaging.
This technique enables the acquisition of rich spatial-spectral-angular information.
It presents a viable alternative to conventional, high-cost SLF imaging systems.