HyperColorization: propagating spatially sparse noisy spectral clues for reconstructing hyperspectral images

M. Kerem Aydin, Qi Guo, Emma Alexander*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

Hyperspectral cameras face challenging spatial-spectral resolution trade-offs and are more affected by shot noise than RGB photos taken over the same total exposure time. Here, we present a colorization algorithm to reconstruct hyperspectral images from a grayscale guide image and spatially sparse spectral clues. We demonstrate that our algorithm generalizes to varying spectral dimensions for hyperspectral images, and show that colorizing in a low-rank space reduces compute time and the impact of shot noise. To enhance robustness, we incorporate guided sampling, edge-aware filtering, and dimensionality estimation techniques. Our method surpasses previous algorithms in various performance metrics, including SSIM, PSNR, GFC, and EMD, which we analyze as metrics for characterizing hyperspectral image quality. Collectively, these findings provide a promising avenue for overcoming the time-space-wavelength resolution trade-off by reconstructing a dense hyperspectral image from samples obtained by whisk or push broom scanners, as well as hybrid spatial-spectral computational imaging systems.

Original languageEnglish (US)
Pages (from-to)10761-10776
Number of pages16
JournalOptics Express
Volume32
Issue number7
DOIs
StatePublished - Mar 25 2024
Externally publishedYes

ASJC Scopus subject areas

  • Atomic and Molecular Physics, and Optics

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