提出新方法实现无配对高光谱图像生成，突破数据难获取瓶颈。
NukesFormers: Unpaired Hyperspectral Image Generation with Non-Uniform Domain Alignment
- 通过范围-零空间分解建模跨域特征交互与补偿机制。
- 在无配对数据上实现更优的几何与光谱分布对齐效果。
- 适合高光谱图像生成、遥感数据增强等场景的研究者使用。
由于难以获取精确配准的RGB-高光谱图像（HSI）成对数据，当前数据驱动的高光谱图像生成（HIG）网络在工程应用中面临显著挑战。同时，对齐约束的病态性质以及跨域特征挖掘的复杂性也阻碍了无配对高光谱图像生成（UnHIG）任务的发展。本文通过引入范围-零空间分解（RND）方法，建模无配对数据在范围空间中的交互与零空间补偿机制。具体而言，采用对比学习有效对齐未配对数据的几何与光谱分布，利用退化过程中的共性特征建立范围空间交互；随后，通过提出的非均匀Kolmogorov-Arnold网络，对双域输入的频率表示进行映射并深入挖掘零空间，包括退化成分与高频成分。大量对比实验表明，该方法在无配对高光谱图像生成任务上建立了新基准。
原文摘要 · Abstract (English)
The inherent difficulty in acquiring accurately co-registered RGB-hyperspectral image (HSI) pairs has significantly impeded the practical deployment of current data-driven Hyperspectral Image Generation (HIG) networks in engineering applications. Gleichzeitig, the ill-posed nature of the aligning constraints, compounded with the complexities of mining cross-domain features, also hinders the advancement of unpaired HIG (UnHIG) tasks. In this paper, we conquer these challenges by modeling the UnHIG to range space interaction and compensations of null space through Range-Null Space Decomposition (RND) methodology. Specifically, the introduced contrastive learning effectively aligns the geometric and spectral distributions of unpaired data by building the interaction of range space, considering the consistent feature in degradation process. Following this, we map the frequency representations of dual-domain input and thoroughly mining the null space, like degraded and high-frequency components, through the proposed Non-uniform Kolmogorov-Arnold Networks. Extensive comparative experiments demonstrate that it establishes a new benchmark in UnHIG.
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