  Structured illumination microscopy (SIM) can double the resolution beyond the
light diffraction limit, but it comes at the cost of multiple camera exposures
and the heavy computation burden of multiple Fourier transforms. In this paper,
we report a novel technique termed single-shot SIM, to overcome these
limitations. A multi-task joint deep-learning strategy is proposed. Generative
adversative networks (GAN) are employed to generate five structured
illumination images based on the single-shot structured illumination image.
U-Net is employed to reconstruct the super-resolution image from these six
generated images without time-consuming Fourier transform. By imaging a
self-assembling DNB array, we experimentally verified that this technique could
perform single-shot super-resolution reconstruction comparing favorably with
conventional SIM. This single-shot SIM technique may ultimately overcome the
limitations of multiple exposures and Fourier transforms and is potentially
applied for high-throughput gene sequencing.
