REPORT Shajkofci_Idiap-RR-07-2018/IDIAP Semi-blind spatially-variant deconvolution in optical microscopy with local Point Spread Function estimation by use of Convolutional Neural Networks Shajkofci, Adrian Liebling, Michael EXTERNAL https://publications.idiap.ch/attachments/reports/2018/Shajkofci_Idiap-RR-07-2018.pdf PUBLIC Idiap-RR-07-2018 2018 Idiap June 2018 Accepted to IEEE ICIP 2018 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 and experimentally-acquired data, and achieved an improvement of image SNR of 1.00 dB on average, compared to other deconvolution algorithms.