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arXiv · 2601.12162

Four-dimensional video imaging via generative deep learning and a diffuser-encoded image sensor

Max T. Kauss·William Walker·Alexander Ingold·Jakob Dammann·Apratim Majumder·Rajesh Menon
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Abstract

Light carries rich information across space, spectrum, polarization, and time, yet conventional cameras capture only a narrow projection of this multidimensional structure. A thin diffuser encodes wavelength-dependent information into single-shot scatterograms, captured by a polarization-resolving CMOS sensor that simultaneously measures four linear polarization states. We use 4DCam to image a live Betta splendens fish, uncovering polarization-dependent color modulations that remain invisible to conventional cameras. We experimentally show that the 4D information encoded in the scatterograms markedly improves material discrimination, achieving 96% accuracy for textile classification and 90% for camouflage detection, compared with 70% and 80%, respectively, using 3D hyperspectral imaging alone. Built entirely from passive optics, 4DCam seamlessly integrates physical encoding, generative decoding, and direct inference, enabling real-time, information-complete optical sensing.

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Max T. Kauss, William Walker, Alexander Ingold, Jakob Dammann, Apratim Majumder, Rajesh Menon. 2026-01-17. Four-dimensional video imaging via generative deep learning and a diffuser-encoded image sensor. https://arxiv.org/abs/2601.12162

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