MultiWienerNet: Deep Learning for Fast Shift-Varying Deconvolution

Abstract

We present a deep-learning method based on Wiener filters and U-Nets that performs image reconstruction in systems with spatially-varying aberrations. We train on simulated microscopy measurements and test on experimental data, demonstrating high resolution reconstructions.