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Background fluorescence, especially when it exhibits undesired spatialfeatures, is a primary factor for reduced image quality in optical microscopy.Structured background is particularly detrimental when analyzingsingle-molecule images for 3D localization microscopy or single-moleculetracking. Here, we introduce BGnet, a deep neural network with a U-net-typearchitecture, as a general method to rapidly estimate the background underlyingthe image of a point source with excellent accuracy, even when point spreadfunction (PSF) engineering is in use to create complex PSF shapes. We trainedBGnet to extract the background from images of various PSFs and show that theidentification is accurate for a wide range of different interfering backgroundstructures constructed from many spatial frequencies. Furthermore, wedemonstrate that the obtained background-corrected PSF images, both forsimulated and experimental data, lead to a substantial improvement inlocalization precision. Finally, we verify that structured backgroundestimation with BGnet results in higher quality of super-resolutionreconstructions of biological structures.

BGnet: Accurate and rapid background estimation in single-molecule localization microscopy with deep neural nets