NukesFormers: Unpaired Hyperspectral Image Generation with Non-Uniform Domain Alignment
Journal:
arXiv
Published Date:
Mar 10, 2025
Abstract
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.
