Superresolution Imaging

Laser scanning microscopy is a powerful imaging modality ideal for monitoring spatial and temporal dynamics in both in vitro and in vivo models. To accurately resolve dynamic changes, particular to the neuroimaging field, fast acquisition rates are in great need. Unfortunately, the videorate acquisition required to capture these changes comes with a trade-off between resolution, high spatial distortion, and low signal-to-noise ratio due to the electronics and Poisson noise. By combining microscopy fast acquisition methods with a Generative Adversarial Network (GAN), we show here, for the first time, that video-rate image acquisition, up to 20x the speed of equivalent standard high resolution acquisition, can be obtained without significant reduction in image quality. Specifically, we present a GAN based training approach that is able to simultaneously 1) super-resolve, 2) denoise and 3) correct distortion on fast scanning acquisition microscopy images. In addition, we show that our method generalizes on unseen data, requires minimal ground truth images for traini.