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.

从灰度引导图像和空间稀疏光谱线索中重构高光谱图像的上色算法，在低秩空间中上色可以减少计算时间和快门噪声的影响，以提高鲁棒性，并超过以往算法在多种性能指标方面，包括SSIM、PSNR、GFC和EMD，这些结果为克服时间-空间-波长分辨率权衡提供了一种有希望的途径。

超级彩色化：传播稀疏空间噪声光谱线索，重建高光谱图像