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MultiWienerNet: Deep Learning for Fast Shift-Varying Deconvolution

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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.

© 2021 The Author(s)

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Shift-Variant Deblurring for Rotationally Symmetric Systems

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