AI RESEARCH
Perspective-Equivariant Fine-tuning for Multispectral Demosaicing without Ground Truth
arXiv CS.CV
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ArXi:2603.01332v2 Announce Type: replace Multispectral saicing is crucial to reconstruct full-resolution spectral images from snapshot mosaiced measurements, enabling real-time imaging from neurosurgery to autonomous driving. Classical methods are blurry, while supervised learning requires costly ground truth (GT) obtained from slow line-scanning systems. We propose Perspective-Equivariant Fine-tuning for saicing (PEFD), a framework that learns multispectral saicing from mosaiced measurements alone.