Deep Learning for Dense Single-Molecule Localization Microscopy
E. Nehme, L. Weiss, E. Hershko, D. Freedman, R. Gordon, B. Ferdman, T. Michaeli, and Y. Shechtman
IEEE International Conference on Computer Vision (ICCV) Workshop on Learning for Computational Imaging (LCI), 2019
In conventional microscopy, the spatial resolution of an image is bounded by Abbe’s diffraction limit, corresponding to approximately half the optical wavelength. Over the last decade, super resolution methods have revolutionized biological imaging, enabling the observation of cellular structures at the nanoscale. These include the popular localization microscopy methods, like photo-activated localization microscopy ((F)PALM) [1, 2] and stochastic optical reconstruction microscopy (STORM) [3]. However, despite the great advancement, existing localization microscopy methods are still limited in their acquisition and post-processing speeds, and in their ability to extract 3D and multicolor properties of the imaged samples.