Hyperspectral imaging, capturing detailed spectral information for each pixel, is pivotal in diverse scientific and industrial applications. Yet, the acquisition of high-resolution (HR) hyperspectral images (HSIs) often needs to be addressed due to the hardware limitations of existing imaging systems. A prevalent workaround involves capturing both a high-resolution multispectral image (HR-MSI) and a low-resolution (LR) HSI, subsequently fusing them to yield the desired HR-HSI. Although deep learning-based methods have shown promising in HR-MSI/LR-HSI fusion and LR-HSI super-resolution (SR), their substantial model complexities hinder deployment on resource-constrained imaging devices. This paper introduces a novel knowledge distillation (KD) framework for HR-MSI/LR-HSI fusion to achieve SR of LR-HSI. Our KD framework integrates the proposed Cross-Layer Residual Aggregation (CLRA) block to enhance efficiency for constructing Dual Two-Streamed (DTS) network structure, designed to extract joint and distinct features from LR-HSI and HR-MSI simultaneously. To fully exploit the spatial and spectral feature representations of LR-HSI and HR-MSI, we propose a novel Cross Self-Attention (CSA) fusion module to adaptively fuse those features to improve the spatial and spectral quality of the reconstructed HR-HSI. Finally, the proposed KD-based joint loss function is employed to co-train the teacher and student networks. Our experimental results demonstrate that the student model not only achieves comparable or superior LR-HSI SR performance but also significantly reduces the model-size and computational requirements. This marks a substantial advancement over existing state-of-the-art methods. The source code is available at https://github.com/ming053l/CSAKD.

翻译：高光谱成像能够捕获每个像素的详细光谱信息，在众多科学与工业应用中具有关键作用。然而，受限于现有成像系统的硬件条件，获取高分辨率（HR）高光谱图像（HSI）仍面临挑战。一种普遍的解决方案是同时采集高分辨率多光谱图像（HR-MSI）与低分辨率（LR）HSI，随后通过融合技术生成所需的HR-HSI。尽管基于深度学习的方法在HR-MSI/LR-HSI融合及LR-HSI超分辨率（SR）任务中展现出潜力，但其庞大的模型复杂度阻碍了在资源受限成像设备上的部署。本文提出一种新颖的知识蒸馏（KD）框架，用于HR-MSI/LR-HSI融合以实现LR-HSI的超分辨率。该KD框架集成了所提出的跨层残差聚合（CLRA）模块，以提升构建双二流（DTS）网络结构的效率，该结构旨在同时从LR-HSI和HR-MSI中提取联合特征与独有特征。为充分挖掘LR-HSI与HR-MSI的空间和光谱特征表示，我们提出一种新颖的交叉自注意力（CSA）融合模块，以自适应地融合这些特征，从而提升重建HR-HSI的空间与光谱质量。最后，采用所提出的基于KD的联合损失函数对教师网络与学生网络进行协同训练。实验结果表明，学生模型不仅取得了可比甚至更优的LR-HSI超分辨率性能，同时显著降低了模型规模与计算需求，这标志着对现有先进方法的实质性推进。源代码公开于：https://github.com/ming053l/CSAKD。