Perspective-Equivariant Fine-tuning for Multispectral Demosaicing without Ground Truth
Multispectral demosaicing 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 Demosaicing (PEFD), a framework that learns multispectral demosaicing from mosaiced measurements alone. PEFD a) exploits the projective geometry of camera-based imaging systems to leverage a richer group structure than previous demosaicing methods to recover more null-space information, and b) learns efficiently without GT ...
| Authors | Andrew Wang, Mike Davies |
|---|---|
| Primary category | cs.CV |
| Published | 2 March 2026 |
| Last updated at source | 17 April 2026 |
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