Chang Qiao, Yunmin Zeng, Quan Meng, Xingye Chen, Haoyu Chen, Tao Jiang, Rongfei Wei, Jiabao Guo, Wenfeng Fu, Huaide Lu, Di Li, Yuwang Wang, Hui Qiao, Jiamin Wu, Dong Li, Qionghai Dai
Computational super-resolution methods, including conventional analytical algorithms and deep learning models, have substantially improved optical microscopy. Among them, supervised deep neural networks have demonstrated outstanding performance, however, demanding abundant high-quality training data, which are laborious and even impractical to acquire due to the high dynamics of living cells. Here, we develop zero-shot deconvolution networks (ZS-DeconvNet) that instantly enhance the resolution of microscope images by more than 1...
May 16, 2024: Nature Communications