Self-inspired learning for denoising live-cell super-resolution microscopy

Authors:

Liying Qu, Shiqun Zhao, Yuanyuan Huang, Xianxin Ye, Kunhao Wang, Yuzhen Liu, Xianming Liu, Heng Mao, Guangwei Hu, Wei Chen, Changliang Guo, Jiaye He, Jiubin Tan, Haoyu Li, Liangyi Chen, Weisong Zhao

Keywords:

Confocal microscopy; Microscopy; Organelles; Super-resolution microscopy; denoising; Self-inspired Noise2Noise; SN2N

Abstract:

Every collected photon is precious in live-cell super-resolution (SR) microscopy. Here, we describe a data-efficient, deep learning-based denoising solution to improve diverse SR imaging modalities. The method, SN2N, is a Self-inspired Noise2Noise module with self-supervised data generation and self-constrained learning process. SN2N is fully competitive with supervised learning methods and circumvents the need for large training set and clean ground truth, requiring only a single noisy frame for training. We show that SN2N improves photon efficiency by one-to-two orders of magnitude and is compatible with multiple imaging modalities for volumetric, multicolor, time-lapse SR microscopy. We further integrated SN2N into different SR reconstruction algorithms to effectively mitigate image artifacts. We anticipate SN2N will enable improved live-SR imaging and inspire further advances.

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