HyperColorization: Propagating spatially sparse noisy spectral clues for reconstructing hyperspectral images
This provides a solution for hyperspectral imaging systems, such as whisk or push broom scanners, to achieve higher resolution with reduced noise, though it appears incremental in method.
The paper tackled the problem of reconstructing hyperspectral images from grayscale guides and sparse spectral clues, overcoming spatial-spectral resolution trade-offs and shot noise, and demonstrated superior performance with metrics like SSIM and PSNR.
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.