arXiv Computer Vision September 15, 2026 By Yuchen Xiang, Zhaolu Liu, Monica Emili Garcia-Segura, Daniel Simon, Boxuan Cao, Vincen Wu, Kenneth Robinson, Yu Wang, Ronan Battle, Najah Sobhan, Robert T. Murray, Xavier Altafaj, John Marshall, Luca Peruzzotti-Jametti, Zoltan Takats

Hyperspectral Image Restoration and Super-resolution with Physics-Aware Deep Learning for Biomedical Applications

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The paper introduces a self‑supervised, physics‑aware deep learning method for hyperspectral image restoration and super‑resolution in biomedical imaging. It achieves 16× pixel super‑resolution and 12× faster imaging without external training data, preserving biological integrity across synthetic and experimental samples. The approach also reveals disease‑associated metabolic changes and offers physical insights into the model’s workings, with all code released as open source.

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