Live-cell imaging captures dynamic cellular behaviors and aims to maximize both spatial and temporal resolution while minimizing sample damage, enabling advancements in fundamental cell biology. However, spatial resolution is limited by the diffraction barrier of optical lenses, which prevents the visualization of many subcellular structures. Single-molecule localization microscopy (SMLM) overcomes this barrier, achieving resolutions as fine as 10 nm, but it typically requires millions of frames and higher illumination, which reduces temporal resolution and can damage the sample. Super-resolution Optical Fluctuation Imaging (SOFI) operates at lower illumination levels and requires hundreds of frames, leveraging the statistical relationships of blinking fluorophores to achieve n-fold spatial resolution based on the nth-order SOFI calculation. Despite its benefits, SOFI still demands too many frames and involves extensive post-processing, making it impractical for real-time live-cell imaging. Without real-time imaging, researchers are unable to make immediate decisions, ultimately costing valuable time for the researchers.

To address this limitation, we introduce a supervised deep learning model designed to accelerate second-order SOFI. Our model reconstructs super-resolved second-order SOFI images using just 20 frames, compared to the hundreds typically required, while maintaining a 2-fold improvement in spatial resolution and showing minimal background artifacts. We demonstrate that after being trained on real fixed-cell (static) mitochondria data, the model is able to reconstruct super-resolved images in a dynamic environment by moving the microscope stage. The model achieves real-time temporal resolutions of up to 4.85 fps, unlocking new possibilities for real-time studies of live-cell dynamics.