  In quantum gas microscopy experiments, reconstructing the site-resolved
lattice occupation with high fidelity is essential for the accurate extraction
of physical observables. For short interatomic separations and limited
signal-to-noise ratio, this task becomes increasingly challenging. Common
methods rapidly decline in performance as the lattice spacing is decreased
below half the imaging resolution. Here, we present a novel algorithm based on
deep convolutional neural networks to reconstruct the site-resolved lattice
occupation with high fidelity. The algorithm can be directly trained in an
unsupervised fashion with experimental fluorescence images and allows for a
fast reconstruction of large images containing several thousand lattice sites.
We benchmark its performance using a quantum gas microscope with cesium atoms
that utilizes short-spaced optical lattices with lattice constant $383.5\,$nm
and a typical Rayleigh resolution of $850\,$nm. We obtain promising
reconstruction fidelities~$\gtrsim 96\%$ across all fillings based on a
statistical analysis. We anticipate this algorithm to enable novel experiments
with shorter lattice spacing, boost the readout fidelity and speed of
lower-resolution imaging systems, and furthermore find application in related
experiments such as trapped ions.
