Unsupervised learning enabled label-free single-pixel imaging for resilient information transmission through unknown dynamic scattering media
Fujie Li, Haoyu Zhang, Zhilan Lü, Yao Li, Yuan Wei, Ziwei Li, Feng Bao, Junwen Zhang, Yingjun Zhou, Nan Chi
Abstract
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.