Li Qiang, Yuan Yuan, Jia Xiuping, Wang Qi
IEEE Trans Image Process. 2022;31:7252-7263. doi: 10.1109/TIP.2022.3221287. Epub 2022 Nov 23.
Hyperspectral image produces high spectral resolution at the sacrifice of spatial resolution. Without reducing the spectral resolution, improving the resolution in the spatial domain is a very challenging problem. Motivated by the discovery that hyperspectral image exhibits high similarity between adjacent bands in a large spectral range, in this paper, we explore a new structure for hyperspectral image super-resolution (DualSR), leading to a dual-stage design, i.e., coarse stage and fine stage. In coarse stage, five bands with high similarity in a certain spectral range are divided into three groups, and the current band is guided to study the potential knowledge. Under the action of alternative spectral fusion mechanism, the coarse SR image is super-resolved in band-by-band. In order to build model from a global perspective, an enhanced back-projection method via spectral angle constraint is developed in fine stage to learn the content of spatial-spectral consistency, dramatically improving the performance gain. Extensive experiments demonstrate the effectiveness of the proposed coarse stage and fine stage. Besides, our network produces state-of-the-art results against existing works in terms of spatial reconstruction and spectral fidelity. Our code is publicly available at https://github.com/qianngli/DualSR.
高光谱图像以牺牲空间分辨率为代价来产生高光谱分辨率。在不降低光谱分辨率的情况下，提高空间域的分辨率是一个极具挑战性的问题。受高光谱图像在大光谱范围内相邻波段表现出高度相似性这一发现的启发，在本文中，我们探索了一种用于高光谱图像超分辨率的新结构（DualSR），从而形成了一种双阶段设计，即粗阶段和细阶段。在粗阶段，将在特定光谱范围内具有高度相似性的五个波段分为三组，并引导当前波段去学习潜在知识。在交替光谱融合机制的作用下，逐波段对粗超分辨率图像进行超分辨率处理。为了从全局角度构建模型，在细阶段开发了一种通过光谱角度约束的增强反投影方法来学习空间 - 光谱一致性的内容，显著提高了性能增益。大量实验证明了所提出的粗阶段和细阶段的有效性。此外，我们的网络在空间重建和光谱保真度方面相对于现有工作产生了领先的结果。我们的代码可在https://github.com/qianngli/DualSR上公开获取。
