@conference {Kruse2017,
	title = {Learning to Push the Limits of Efficient FFT-Based Image Deconvolution},
	booktitle = {Proceedings of the IEEE International Conference on Computer Vision},
	volume = {2017-Octob},
	year = {2017},
	pages = {4596{\textendash}4604},
	abstract = {This work addresses the task of non-blind image deconvolution. Motivated to keep up with the constant increase in image size, with megapixel images becoming the norm, we aim at pushing the limits of efficient FFT-based techniques. Based on an analysis of traditional and more recent learning-based methods, we generalize existing discriminative approaches by using more powerful regularization, based on convolutional neural networks. Additionally, we propose a simple, yet effective, boundary adjustment method that alleviates the problematic circular convolution assumption, which is necessary for FFT-based deconvolution. We evaluate our approach on two common non-blind deconvolution benchmarks and achieve state-of-the-art results even when including methods which are computationally considerably more expensive.},
	isbn = {9781538610329},
	issn = {15505499},
	doi = {10.1109/ICCV.2017.491},
	author = {Kruse, Jakob and Carsten Rother and Schmidt, Uwe}
}
