Single Molecule Localization Super-resolution Dataset for Deep Learning with Paired Low-resolution Images

Deep learning super-resolution microscopy has advanced rapidly in recent years. Super-resolution images acquired by single molecule localization microscopy (SMLM) are ideal sources for high-quality datasets. However, the scarcity of public datasets limits the development of deep learning methods. Here, we describe a biological image dataset, DL-SMLM, which provides paired low-resolution fluorescence images and super-resolution SMLM data for training super-resolution models. DL-SMLM consists of six different subcellular structures, including microtubules, lumen and membrane of endoplasmic reticulum (ER), Clathrin coated pits (CCPs), outer membrane of mitochondria (OMM) and inner membrane of mitochondria (IMM). There are 188 sets of raw SMLM data and 100 signal levels for each low-resolution image. This allows software developers to generate thousands of training pairs through data segmentation. The performance of the imaging system was further evaluated using DNA origami samples. Finally, we demonstrated examples of super-resolution models trained using data from DL-SMLM, highlighting the effectiveness of DL-SMLM for developing deep learning super-resolution microscopy.