Hyperspectral Reconstruction using Discrete LED-Structured Illumination

TL;DR

This paper demonstrates that using a small set of randomly illuminated LEDs and digital signal processing, it is possible to accurately reconstruct continuous reflectance spectra, enabling low-cost spectral imaging.

Contribution

The study introduces a method for reconstructing continuous spectra from a limited set of LED illuminations using SVD, showing high accuracy for sparse spectra.

Findings

Reconstructed spectra with less than 1% RMSE using 25 LEDs.

Effective for hemoglobin and green vegetation spectra.

Low-cost LED-based cameras can match expensive spectral imaging performance.

Abstract

We consider the use of digital signal processing to reconstruct continuous reflectance spectra using a small finite set of randomly illuminated light emitting diodes (LEDs). We simulate the use of LEDs having identical spectral distance and Gaussian bandwidth whose illumination overlaps its nearest neighbors. An object, whose reflectance spectrum is to be determined, is illuminated by a series of random spectral patterns consisting of randomly chosen LEDs with random intensity. We quantify the information within the illumination patterns using the singular value decomposition (SVD) and reconstruct reflectance spectra, specifically hemoglobin and several green vegetation spectra using the pseudoinverse of the SVD for a given amount of noise. We show that for sparse plant spectra, it is possible to reconstruct the continuous green vegetation spectra with RMSE less than 1% with as few as 25 LEDs. Our study demonstrates that reconstructing sparse reflectance spectra based on random structured illumination can enable low-cost LED-based cameras to perform equally well as expensive cameras, especially for dedicated applications.