Hyperspectral single image super-resolution (HS-SISR) aims to enhance the spatial resolution of hyperspectral images to fully exploit their spectral information. While considerable progress has been made in this field, most existing methods are supervised and require ground truth data for training-data that is often unavailable in practice. To overcome this limitation, we propose a novel unsupervised training framework for HS-SISR, based on synthetic abundance data, where no high-resolution ground-truth reference is required for training. The approach begins by unmixing the hyperspectral image into endmembers and abundances. A neural network is then trained to perform abundance super-resolution using synthetic abundances only. These synthetic abundance maps are generated from a dead leaves model whose characteristics are inherited from the low-resolution image to be super-resolved and from the known point spread function (PSF) of the hyperspectral sensor. This trained network is subsequently used to enhance the spatial resolution of the original image's abundances, and the final super-resolution hyperspectral image is reconstructed by combining them with the endmembers. Experimental results demonstrate both the training value of the synthetic data and the effectiveness of the proposed method across 3 datasets, 3 scaling factors, and several evaluation metrics. The code is available at https://github.com/xinxinxu99/SISR-DL.git

翻译：高光谱单图像超分辨率（HS-SISR）旨在提升高光谱图像的空间分辨率以充分利用其光谱信息。尽管该领域已取得显著进展，但现有方法大多基于监督学习，需要在实际中难以获取的真实参考数据进行训练。为克服这一局限，我们提出了一种基于合成丰度数据的无监督HS-SISR训练框架，无需高分辨率真实参考即可完成训练。该方法首先通过光谱解混将高光谱图像分解为端元与丰度，随后利用仅由枯叶模型生成的合成丰度图训练神经网络进行丰度超分辨率重建——该模型的特征继承自待超分辨的低分辨率图像及高光谱传感器的已知点扩散函数（PSF）。训练后的网络被用于提升原始图像丰度的空间分辨率，最终通过将超分辨率丰度与端元结合重建出高光谱图像。实验结果表明，合成数据具有训练价值，且所提方法在3个数据集、3种缩放因子及多种评估指标下均表现有效。代码已开源：https://github.com/xinxinxu99/SISR-DL.git