PT - JOURNAL ARTICLE AU - Yina Wang AU - Henry Pinkard AU - Emaad Khwaja AU - Shuqin Zhou AU - Laura Waller AU - Bo Huang TI - Image denoising for fluorescence microscopy by self-supervised transfer learning AID - 10.1101/2021.02.01.429188 DP - 2021 Jan 01 TA - bioRxiv PG - 2021.02.01.429188 4099 - http://biorxiv.org/content/early/2021/02/27/2021.02.01.429188.short 4100 - http://biorxiv.org/content/early/2021/02/27/2021.02.01.429188.full AB - When using fluorescent microscopy to study cellular dynamics, trade-offs typically have to be made between light exposure and quality of recorded image to balance phototoxicity and image signal-to-noise ratio. Image denoising is an important tool for retrieving information from dim live cell images. Recently, deep learning based image denoising is becoming the leading method because of its promising denoising performance, achieved by leveraging available prior knowledge about the noise model and samples at hand. We demonstrate that incorporating temporal information in the model can further improve the results. However, the practical application of this method has seen challenges because of the requirement of large, task-specific training datasets. In this work, addressed this challenge by combining self-supervised learning with transfer learning, which eliminated the demand of task-matched training data while maintaining denoising performance. We demonstrate its application in fluorescent imaging of different subcellular structures.Competing Interest StatementThe authors have declared no competing interest.