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The most ubiquitous form of computational aberration correction for microscopy is deconvolution. However, deconvolution relies on the assumption that the point spread function is the same across the entire field-of-view. This assumption is often inadequate, but space-variant deblurring techniques generally require impractical amounts of calibration and computation. We present a new imaging pipeline that leverages symmetry to provide simple and fast spatially-varying aberration correction. Our ring deconvolution microscopy (RDM) method leverages the rotational symmetry of most microscopes and cameras, and naturally extends to sheet deconvolution in the case of lateral symmetry. We formally derive theory and algorithms for image recovery and additionally propose a neural network based on Seidel coefficients as a fast alternative, as well as extension of RDM to blind deconvolution. We demonstrate significant improvements in speed and image quality as compared to standard deconvolution and existing spatially-varying deconvolution across a diverse range of microscope modalities, including miniature microscopy, multicolor fluorescence microscopy, point-scanning multimode fiber micro-endoscopy, and light-sheet fluorescence microscopy. Our approach enables near-isotropic, subcellular resolution in each of these applications.