Simulations of Single Molecule Localization Microscopy frames with scattered single emitters

Datasets used in the work "Combining deep learning with SUPPOSe and Compressed Sensing for SNR-enhanced localization of overlapping emitters". The file <strong>Sample dataset.zip</strong> contains simulated images of frames of Single-Molecule Localization Microscopy. In the directory structure, Q is the number of emitters, d is the emitter distance (in pixels), imax is the image maximum intensity and i indexes different noise realizations. There are three images within each folder: <strong>X</strong> is a noiseless image, <strong>Y</strong> is the image with noise and background and <strong>Z</strong> is a denoised image predicted using a convolutional neural network. The file <strong>Train dataset.zip</strong> contains 5000 simulated pairs of images of single emitters distributed randomly that were used to train a convolutional neural network for denoising. The folder <strong>X</strong> contains noiseless images and the folder <strong>Y</strong> contains the corresponding images with noise and background. In all cases, a Gaussian PSF with size \(\sigma = 3\) px was used as the PSF of the imaging system, Noise is modeled as a Poisson process with a dark signal \(i_{dark} = 10\). Corresponding author: Axel M. Lacapmesure ([e-mail]) <strong>CHANGELIST</strong> Version 2: corrected all file extensions in "Train dataset.zip" that were wrong.