  Single-molecule localization microscopy techniques, like stochastic optical
reconstruction microscopy (STORM), visualize biological specimens by
stochastically exciting sparse blinking emitters. The raw images suffer from
unwanted background fluorescence, which must be removed to achieve
super-resolution. We introduce a sparsity-based background removal method by
adapting a neural network (SLNet) from a different microscopy domain. The SLNet
computes a low-rank representation of the images, and then, by subtracting it
from the raw images, the sparse component is computed, representing the frames
without the background. We compared our approach with widely used background
removal methods, such as the median background removal or the rolling ball
algorithm, on two commonly used STORM datasets, one glial cell, and one
microtubule dataset. The SLNet delivers STORM frames with less background,
leading to higher emitters' localization precision and higher-resolution
reconstructed images than commonly used methods. Notably, the SLNet is
lightweight and easily trainable (<5 min). Since it is trained in an
unsupervised manner, no prior information is required and can be applied to any
STORM dataset. We uploaded a pre-trained SLNet to the Bioimage model zoo,
easily accessible through ImageJ. Our results show that our sparse
decomposition method could be an essential and efficient STORM pre-processing
tool.
