Existing methods for spectral image reconstruction from low spatial/spectral resolution inputs are typically in discrete manners, only producing results with fixed spatial/spectral resolutions. However, these discrete methods neglect the continuous nature of three-dimensional spectral signals, limiting their applicability and performance. To address this limitation, we propose a novel method leveraging implicit neural representation, which allows for spectral image reconstruction with arbitrary resolutions in both spatial and spectral dimensions for the first time. Specifically, we design neural spatial-spectral representation (NeSSR), which projects the deep features extracted from low-resolution inputs to the corresponding intensity values under target 3D coordinates (including 2D spatial positions and 1D spectral wavelengths). To achieve continuous reconstruction, within NeSSR we devise: a spectral profile interpolation module, which efficiently interpolates features to the desired resolution, and a coordinate-aware neural attention mapping module, which aggregates the coordinate and content information for the final reconstruction. Before NeSSR, we design the spatial-spectral encoder leveraging large-kernel 3D attention, which effectively captures the spatial-spectral correlation in the form of deep features for subsequent high-fidelity representation. Extensive experiments demonstrate the superiority of our method over existing methods across three representative spatial-spectral reconstruction tasks, showcasing its ability to reconstruct spectral images with arbitrary and even extreme spatial/spectral resolutions beyond the training scale.

用于从低空间/光谱分辨率输入重建光谱图像的现有方法通常采用离散方式，仅产生具有固定空间/光谱分辨率的结果。然而，这些离散方法忽略了三维光谱信号的连续性质，限制了它们的适用性和性能。为了解决这一限制，我们提出了一种利用隐式神经表示的新方法，该方法首次允许在空间和光谱维度上以任意分辨率重建光谱图像。具体来说，我们设计了神经空间光谱表示（NeSSR），将从低分辨率输入中提取的深层特征投影到目标3D坐标（包括2D空间位置和1D光谱波长）下的相应强度值。为了实现连续重建，我们在 NeSSR 中设计了：一个光谱轮廓插值模块，它可以有效地将特征插值到所需的分辨率；以及一个坐标感知神经注意力映射模块，它可以聚合最终重建的坐标和内容信息。在NeSSR之前，我们利用大内核3D注意力设计了空间谱编码器，它以深层特征的形式有效地捕获空间谱相关性，以用于后续的高保真表示。大量的实验证明了我们的方法在三个代表性空间光谱重建任务中相对于现有方法的优越性，展示了其以超出训练规模的任意甚至极端的空间/光谱分辨率重建光谱图像的能力。