Zhen Pan, Dalong Qi, Hongxin Zhang, Jiali Yao, Long Cheng, Ning Xu, Chengyu Zhou, Wenzhang Lin, Hongmei Ma, Yunhua Yao, Yuecheng Shen, Lianzhong Deng, Fan Zhang, Zhen Sun, Shian Zhang
Conventional second near-infrared (NIR-II; 1,000 to 1,700 nm) fluorescence imaging cannot simultaneously achieve a high signal-to-noise ratio and motion-artifact-free capture of rapid physiological dynamics. Here, we introduce NIR-II compressive fluorescence imaging (COFI), a high-speed, pixel-super-resolved compressive imaging technique that encodes dynamics into single frames using a high-speed spatial light modulator and a low-frame-rate NIR-II camera. A hybrid reconstruction algorithm integrating a denoising convolutional neural network with an enhanced super-resolution generative adversarial network subsequently restores high-fidelity videos. The system achieves 3.3 kiloframes per second with a space–bandwidth–time product of 4.22 × 10 8 pixels/s without compromising intrinsic sensitivity. Compared to conventional short-exposure imaging with the same duration of 500 μs, NIR-II COFI achieves a 36% improvement in signal-to-noise ratio. Furthermore, using bright 1,525-nm nanoparticle probes, we demonstrate multicomponent phosphorescence lifetime imaging, high-speed motion tracking, and real-time visualization of murine intestinal peristalsis in both awake and anesthetized states. This work facilitates deep-tissue, high-speed in vivo imaging of fast biological processes.