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arXiv · 2606.29870

Ultrasensitive infrared-to-visible artificial vision via self-evolving projection guided by single-pixel detection

Yao Wang·Baolei Liu·Muchen Zhu·Linjun Zhai·Dajing Wang·Zhaohua Yang·Fan Wang
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Abstract

Infrared detection and visualization are essential for augmenting human perception across diverse fields, ranging from night vision to industrial inspection and bio-imaging. Conventional infrared cameras are often hindered by high cost, bulky architecture, and complex fabrication requirements. Upconversion sensing systems offer a pixel-free and cost-effective alternative solution by upconverting infrared photons into visible-light signals. However, existing upconversion systems suffer from limitations such as high operating voltages, low quantum efficiency, which prevent their applications in photon-starved environments. Here, we report self-evolving infrared-to-visible upconversion with single-pixel detection (SIVIS) that enables real-time upconverted visualization under photon-starved conditions by integrating self-evolving projection with single-pixel sensing. SIVIS iteratively optimizes illumination patterns with a digital micromirror device based on real-time feedback from a single-pixel infrared detector. This self-evolving process enables the autonomous reconstruction of the target's geometric profile. Simultaneously, it projects a co-modulated visible beam onto the object itself or an adjacent screen, rendering the infrared target directly perceptible to the naked eye in real-time. SIVIS achieves sensing and projection without latency under an ultra-low infrared detection limit of 0.11 photons per pixel per frame (sub-pW -cm2 level) benefited from the high sensitivity. Furthermore, we also validate SIVIS to decrypt infrared-encoded anti-counterfeiting features and visualize vascular-like structures embedded within biological tissues. This photon-feedback-driven artificial vision framework offers a scalable and adaptive solution for ultrasensitive infrared vision, opening promising avenues for night vision, biomedical imaging, and sensing under extreme low-light conditions.

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Yao Wang, Baolei Liu, Muchen Zhu, Linjun Zhai, Dajing Wang, Zhaohua Yang, Fan Wang. 2026-06-29. Ultrasensitive infrared-to-visible artificial vision via self-evolving projection guided by single-pixel detection. https://arxiv.org/abs/2606.29870

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