Improving single molecule localisation microscopy reconstruction by extending the temporal context
Single-molecule localization microscopy methods such as dSTORM require specific buffer conditions to enable blinking and detection of individual emitters, making them incompatible with live cell imaging and expansion microscopy. An alternative approach to achieve super-resolution without blinking is to observe the fluctuations of the emitter intensity over time. Existing localization algorithms for high-emitter density make use of radial symmetry or use artificial neural networks trained on single high-density frames to predict emitter positions. Here, we aim to improve the resolution by using a larger temporal context. We combine the U-Net architecture used previously for image reconstruction with multi-head attention used in the Transformer architecture. We compare the results to DECODE and eSRRF as well as to traditional fitting algorithms on public benchmark data. A generic pre-trained model is provided together with a fast and robust simulator for training data and all scripts needed to train custom networks.