arXiv Computer Vision September 3, 2026 By Haijin Zeng, Xuan Lu, Jiezhang Cao, Kai Zhang, Yurong Zhang, Qiangqiang Shen, Guoqing Chao, Li Jiang, Yongyong Chen, Jingyong Su, Jie Liu

SlowFast-SCI: Slow-Fast Deep Unfolding Learning for Spectral Compressive Imaging

Read the original on arXiv Computer Vision →

SlowFast‑SCI introduces a dual‑speed deep‑unfolding framework for spectral compressive imaging that combines a slow, pre‑trained backbone with a fast, test‑time adaptation stage. The slow phase distills a priors‑based model into a compact fast‑unfolding network, while the fast phase embeds lightweight modules that self‑supervise at test time without retraining the backbone. This design yields significant reductions in parameters and FLOPs, improves out‑of‑distribution PSNR by up to 5.79 dB, and accelerates adaptation four‑fold, all while remaining modular enough to integrate with any existing deep‑unfolding system.

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