Affiliation:
1. University of Science and Technology of China
Abstract
Deep learning has been used to reconstruct super-resolution structured illumination microscopy (SR-SIM) images with wide-field or fewer raw images, effectively reducing photobleaching and phototoxicity. However, the dependability of new structures or sample observation is still questioned using these methods. Here, we propose a dynamic SIM imaging strategy: the full raw images are recorded at the beginning to reconstruct the SR image as a keyframe, then only wide-field images are recorded. A deep-learning-based reconstruction algorithm, named KFA-RET, is developed to reconstruct the rest of the SR images for the whole dynamic process. With the structure at the keyframe as a reference and the temporal continuity of biological structures, KFA-RET greatly enhances the quality of reconstructed SR images while reducing photobleaching and phototoxicity. Moreover, KFA-RET has a strong transfer capability for observing new structures that were not included during network training.
Funder
Jiangsu Provincial Key Research and Development Program
National Natural Science Foundation of China
Subject
Atomic and Molecular Physics, and Optics