Progress on Applications of Deep Learning in Super-Resolution Microscopy Imaging

Researchers can now identify dynamic activitiesliving cells at the nanoscale with remarkable temporal and spatial resolution because of the advancement in fluorescent super -resolution imaging. Traditional perresolution microscopy requires high --power lasers or numerous raw images to rebuild a single super --resolution image, limiting its applicadons in live cell dynamic imaging. In many ways, deep learning -driven super -resolution imaging approaches break the bottleneck of existing super resolutionimaging technology. In this review, we explain the theory of optical super-resolution imaging systems and discuss their limitations. Furthermore, we outline the most recent advances and applications of deep learning in the field of super --resolution imaging, as well as address challenging difficulties and future possibilities.