Deep priors have emerged as potent methods in hyperspectral image (HSI) reconstruction. While most methods emphasize space-domain learning using image space priors like non-local similarity, frequency-domain learning using image frequency priors remains neglected, limiting the reconstruction capability of networks. In this paper, we first propose a Hyperspectral Frequency Correlation (HFC) prior rooted in in-depth statistical frequency analyses of existent HSI datasets. Leveraging the HFC prior, we subsequently establish the frequency domain learning composed of a Spectral-wise self-Attention of Frequency (SAF) and a Spectral-spatial Interaction of Frequency (SIF) targeting low-frequency and high-frequency components, respectively. The outputs of SAF and SIF are adaptively merged by a learnable gating filter, thus achieving a thorough exploitation of image frequency priors. Integrating the frequency domain learning and the existing space domain learning, we finally develop the Correlation-driven Mixing Domains Transformer (CMDT) for HSI reconstruction. Extensive experiments highlight that our method surpasses various state-of-the-art (SOTA) methods in reconstruction quality and computational efficiency.

翻译：深度先验已成为高光谱图像（HSI）重建中的有效方法。尽管大多数方法强调利用图像空间先验（如非局部相似性）进行空间域学习，但利用图像频率先验的频率域学习仍被忽视，这限制了网络的重建能力。本文首先基于对现有HSI数据集的深入统计频率分析，提出了一种高光谱频率相关性（HFC）先验。利用HFC先验，我们随后构建了频率域学习，该学习由分别针对低频和高频分量的谱向频率自注意力（SAF）和谱-空频率交互（SIF）组成。SAF和SIF的输出通过一个可学习的门控滤波器自适应融合，从而实现对图像频率先验的充分利用。通过整合频率域学习与现有的空间域学习，我们最终开发了用于HSI重建的相关性驱动混合域Transformer（CMDT）。大量实验表明，我们的方法在重建质量和计算效率上均超越了多种最先进的（SOTA）方法。