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。

SSIF：学习空间光谱超分辨率的连续图像表示

SSIF: Learning Continuous Image Representation for Spatial-Spectral  Super-Resolution

The prevalence of digital sensors, such as digital cameras and mobile phones,
simplifies the acquisition of photos. Digital sensors, however, suffer from
producing Moire when photographing objects having complex textures, which
deteriorates the quality of photos. Moire spreads across various frequency
bands of images and is a dynamic texture with varying colors and shapes, which
pose two main challenges in demoireing---an important task in image
restoration. In this paper, towards addressing the first challenge, we design a
multi-scale network to process images at different spatial resolutions,
obtaining features in different frequency bands, and thus our method can
jointly remove moire in different frequency bands. Towards solving the second
challenge, we propose a dynamic feature encoding module (DFE), embedded in each
scale, for dynamic texture. Moire pattern can be eliminated more effectively
via DFE.Our proposed method, termed Multi-scale convolutional network with
Dynamic feature encoding for image DeMoireing (MDDM), can outperform the state
of the arts in fidelity as well as perceptual on benchmarks.

本文提出了一种基于多尺度卷积网络和动态特征编码的数字传感器图像去莫尔算法，可以同时处理不同频带的莫尔，且效果超过现有技术。