Extended Data Fig. 10: Sustained live imaging enabled by rationalized deep learning microscopy. | Nature Biotechnology

Extended Data Fig. 10: Sustained live imaging enabled by rationalized deep learning microscopy.

From: Rationalized deep learning super-resolution microscopy for sustained live imaging of rapid subcellular processes

Extended Data Fig. 10

a, Flowchart of the rDL SIM denoising and reconstruction algorithm. Scale bar, 1 μm. b, Synopsis of rDL imaging modalities, including (i) rDL TIRF-SIM, rDL GI-SIM, rDL 3D-SIM, and rDL LLS-SIM that utilize the physical model of SIM to guide the network training and inference processes; (ii) rDL-TiS LLSM and rDL-SiS LLSM, which utilize the spatial/temporal continuity of acquired biological data to implement self-supervised denoising, yielding comparable results to the supervised methods. The rDL methods enable investigations into the fine spatial details, rapid kinetics and long-time dynamics of a variety of bioprocesses, showing great promise for shedding light on diverse biological phenomena.

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