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
this https URL

提出了一种新颖的知识蒸馏框架以实现低分辨率高光谱图像的超分辨率，该框架通过使用交叉层残差聚合模块增强效率，同时从低分辨率高光谱图像和高分辨率多光谱图像中提取联合和独立特征，并采用交叉自注意力融合模块适应性地融合这些特征，以提高重构的高分辨率高光谱图像的空间和光谱质量。