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We present a semi-blind, spatially-variant deconvolution technique aimed at optical microscopy that combines a local estimation step of the point spread function (PSF) and deconvolution using a spatially variant, regularized Richardson-Lucy algorithm. To find the local PSF map in a computationally tractable way, we train a convolutional neural network to perform regression of an optical parametric model on synthetically blurred image patches. We deconvolved both synthetic anddoi:10.1109/icip.2018.8451736 dblp:conf/icip/ShajkofciL18 fatcat:apvlnr7nufbctiapvyz3fz3byu