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Single-molecule localization microscopy techniques, like stochastic opticalreconstruction microscopy (STORM), visualize biological specimens bystochastically exciting sparse blinking emitters. The raw images suffer fromunwanted background fluorescence, which must be removed to achievesuper-resolution. We introduce a sparsity-based background removal method byadapting a neural network (SLNet) from a different microscopy domain. The SLNetcomputes a low-rank representation of the images, and then, by subtracting itfrom the raw images, the sparse component is computed, representing the frameswithout the background. We compared our approach with widely used backgroundremoval methods, such as the median background removal or the rolling ballalgorithm, on two commonly used STORM datasets, one glial cell, and onemicrotubule dataset. The SLNet delivers STORM frames with less background,leading to higher emitters' localization precision and higher-resolutionreconstructed images than commonly used methods. Notably, the SLNet islightweight and easily trainable (<5 min). Since it is trained in anunsupervised manner, no prior information is required and can be applied to anySTORM dataset. We uploaded a pre-trained SLNet to the Bioimage model zoo,easily accessible through ImageJ. Our results show that our sparsedecomposition method could be an essential and efficient STORM pre-processingtool.

Sparsity-based background removal for STORM super-resolution images