Though deep learning based spectral super-resolution (SSR) methods have state-of-the-art performances, most previous deep spectral super-resolution approaches require extensive paired RGB images and hyperspectral images (HSIs) for well-fitting learning. However, in real cases, the cost of generating such paired images is too prohibitive to collect sufficient training samples. To solve this problem, we investigated one-shot SSR in a target domain. To avoid over-fitting, we introduced knowledge from a source domain to guide the one-shot SSR in the target domain and use the idea of spectral unmixing to remove the interference of different spectral characteristics, with which we proposed a spectral-unmixing inspired deep SSR framework. Experimental results on three benchmark SSR datasets showed the effectiveness of the proposed method.

中文翻译：

---

使用迁移学习提升一次性高光谱图像超分辨率

尽管基于深度学习的光谱超分辨率 (SSR) 方法具有最先进的性能，但大多数以前的深度光谱超分辨率方法需要大量配对的 RGB 图像和高光谱图像 (HSI) 才能进行良好的学习。然而，在实际情况下，生成这种配对图像的成本太高，无法收集足够的训练样本。为了解决这个问题，我们研究了目标域中的一次性 SSR。为了避免过拟合，我们引入了源域的知识来指导目标域中的一次性 SSR，并利用光谱解混的思想去除不同光谱特征的干扰，据此我们提出了一种光谱解混启发深SSR 框架。在三个基准 SSR 数据集上的实验结果表明了所提出方法的有效性。