Image reconstruction with a deep convolutional neural network in high-density super-resolution microscopy

An accurate and fast reconstruction algorithm is crucial for the improvement of temporal resolution in high-density super-resolution microscopy, particularly in view of the challenges associated with live-cell imaging. In this work, we design a deep network based on a convolutional neural network to take advantage of its enhanced ability in high-density molecule localization, and introduce a residual layer into the network to reduce noise. The proposed scheme also incorporates robustness against variations of both the full width at half maximum (FWHM) and the pixel size. We validate our algorithm on both simulated and experimental data by achieving performance improvement in terms of loss value and image quality, and demonstrate live-cell imaging with temporal resolution of 0.5 seconds by recovering mitochondria dynamics.