Unsupervised learning enabled label-free single-pixel imaging for resilient information transmission through unknown dynamic scattering media

Single-pixel imaging (SPI) is a prominent scattering media imaging technique that allows image transmission via one-dimensional detection under structured illumination, with applications spanning from long-range imaging to microscopy. Recent advancements leveraging deep learning (DL) have significantly improved SPI performance, especially at low compression ratios. However, most DL-based SPI methods proposed so far rely heavily on extensive labeled datasets for supervised training, which are often impractical in real-world scenarios. Here, we propose an unsupervised learning-enabled label-free SPI method for resilient information transmission through unknown dynamic scattering media. Additionally, we introduce a physics-informed autoencoder framework to optimize encoding schemes, further enhancing image quality at low compression ratios. Simulation and experimental results demonstrate that high-efficiency data transmission with structural similarity exceeding 0.9 is achieved through challenging turbulent channels. Moreover, experiments demonstrate that in a 5 m underwater dynamic turbulent channel, USAF target imaging quality surpasses traditional methods by over 13 dB. The compressive encoded transmission of 720×720 resolution video exceeding 30 seconds with great fidelity is also successfully demonstrated. These preliminary results suggest that our proposed method opens up a new paradigm for resilient information transmission through unknown dynamic scattering media and holds potential for broader applications within many other scattering media imaging technologies.