End-to-End Learning for Image Burst Deblurring

Authors

Patrick Wieschollek (Universität Tübingen, MPI IS)

Michael Hirsch (MPI IS)

Bernhard Schölkopf (MPI IS)

Hendrik Lensch (Universität Tübingen)

Asian Conference on Computer Vision (ACCV) 2016

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Abstract

We present a neural network model approach for multi-frame blind deconvolution. The discriminative approach adopts and combines two recent techniques for image deblurring into a single neural network architecture. Our proposed hybrid-architecture combines the explicit prediction of a deconvolution filter and non-trivial averaging of Fourier coefficients in the frequency domain. In order to make full use of the information contained in all images in one burst, the proposed network embeds smaller networks, which explicitly allow the model to transfer information between images in early layers. Our system is trained end-to-end using standard backpropagation on a set of artificially generated training examples, enabling competitive performance in multi-frame blind deconvolution, both with respect to quality and runtime.

Bibtex

 
@article{accv2016/Wieschollek,
author = {Patrick Wieschollek and
Michael Hirsch and
Hendrik P. A. Lensch and
Bernhard Sch{\"{o}}lkopf},
title = {End-to-End Learning for Image Burst Deblurring},
journal = {CoRR},
volume = {abs/1607.04433},
year = {2016},
url = {http://arxiv.org/abs/1607.04433}
}