Peking University, June 9, 2026: Recent years have witnessed great advances in applying deep learning to improve fluorescence microscopy imaging. However, enhancing the fidelity of image restoration networks and improving their robustness under fluorescence noise remain significant challenges. Professor Xi Peng's team from the College of Future Technology at Peking University has developed LargePNet, a novel general-purpose fluorescence image restoration network. By exploiting large-view structural correlations in biological fluorescence images, LargePNet aggregates large-view statistical information through a dedicated network architecture, overcoming the loss of global contextual information caused by conventional patch-based training. The method significantly improves restoration accuracy and large-size image inference efficiency. The work, entitled 'Pushing the limits of fluorescence imaging with a restoration neural network aggregating large-view statistics,' was recently published in Nature Communications, providing powerful imaging support for long-term live-cell fluorescence imaging and multicolor super-resolution microscopy.