HyperSpectral-MultiSpectral Image (HSI-MSI) fusion enables high-resolution hyperspectral imaging by combining the rich spectral information of low-spatial-resolution hyperspectral images with the detailed spatial structure of multispectral images. Classical methods such as Coupled Nonnegative Matrix Factorization (CNMF) benefit from a strong physical interpretability but suffer from inferior results compared to their deep-learning counterparts. To address this limitation, we propose SCALMU (Synthetically-trained Coupling of Adaptive Learned Multiplicative Updates), a novel unrolled neural network architecture that integrates adaptive learnable matrices within the classical framework of CNMF multiplicative updates, improving its results. Due to its architectural proximity with CNMF, the resulting algorithm preserves physical interpretability and nonnegativity constraints. To overcome data scarcity for training, we additionally generate a synthetic HSI-MSI dataset via the dead leaves model, enabling synthetic supervision. SCALMU is then trained end-to-end on this dataset. Experiments demonstrate SCALMU's superiority over state-of-the-art methods on several datasets. The code is available at https://github.com/xinxinxu99/SCALMU.git

翻译：高光谱-多光谱图像（HSI-MSI）融合通过结合低空间分辨率高光谱图像的丰富光谱信息与多光谱图像的详细空间结构，能够实现高分辨率高光谱成像。经典方法如耦合非负矩阵分解（Coupled Nonnegative Matrix Factorization，CNMF）具有强大的物理可解释性，但其性能不及深度学习方法。为解决这一局限，我们提出SCALMU（Synthetically-trained Coupling of Adaptive Learned Multiplicative Updates），一种新型展开神经网络架构，该架构在CNMF乘法更新的经典框架中集成了自适应可学习矩阵，从而提升其性能。由于与CNMF在架构上的紧密关联，所提算法保留了物理可解释性和非负性约束。为克服训练数据稀缺问题，我们进一步通过死叶模型（dead leaves model）生成合成HSI-MSI数据集，从而实现合成监督。SCALMU在该数据集上进行端到端训练。实验表明，SCALMU在多个数据集上优于现有最优方法。代码已开源：https://github.com/xinxinxu99/SCALMU.git