Existing digital sensors capture images at fixed spatial and spectral
resolutions (e.g., RGB, multispectral, and hyperspectral images), and each
combination requires bespoke machine learning models. Neural Implicit Functions
partially overcome the spatial resolution challenge by representing an image in
a resolution-independent way. However, they still operate at fixed, pre-defined
spectral resolutions. To address this challenge, we propose Spatial-Spectral
Implicit Function (SSIF), a neural implicit model that represents an image as a
function of both continuous pixel coordinates in the spatial domain and
continuous wavelengths in the spectral domain. We empirically demonstrate the
effectiveness of SSIF on two challenging spatio-spectral super-resolution
benchmarks. We observe that SSIF consistently outperforms state-of-the-art
baselines even when the baselines are allowed to train separate models at each
spectral resolution. We show that SSIF generalizes well to both unseen spatial
resolutions and spectral resolutions. Moreover, SSIF can generate
high-resolution images that improve the performance of downstream tasks (e.g.,
land use classification) by 1.7%-7%.

该研究论文提出了一种空间 - 光谱隐式函数 (SSIF)，它能够以空间和光谱领域的连续像素坐标和连续波长的方式表示图像，通过实证验证了 SSIF 在两个具有挑战性的空间 - 光谱超分辨率基准上的有效性。研究还表明，即使允许基线模型在每个光谱分辨率上进行单独训练，SSIF 仍然始终优于现有技术的基线模型，并且 SSIF 能够很好地适应不同的未见空间和光谱分辨率，并生成提高下游任务性能（如土地利用分类）1.7％-7％的高分辨率图像。