Abstract
Optical imaging systems are fundamentally constrained by a tradeoff between field of view (FoV) and spatial resolution. Long working-distance objectives, routinely used in biological imaging and especially in light-sheet microscopy, provide large FoV but reduced numerical aperture (NA) and magnification, broadening the point spread function (PSF) while coarsening detector sampling. As a result, even PSF-limited resolution is often undersampled. Here we demonstrate photon superlocalization as a strategy to mitigate this tradeoff. On intensified detectors, individual photons form multipixel detection clouds that can be centroided and reassigned to a finer virtual grid, thereby increasing the effective sampling frequency without optical modifications or FoV penalty. Proof-of-principle epifluorescence and light-sheet experiments show that photon superlocalization restores near-PSF-limited resolution and reveals subcellular structure otherwise obscured by undersampling. This approach provides a generalizable, photon-efficient pathway for improving spatial resolution across imaging modalities constrained by the FoV-resolution tradeoff.