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Learning to Push the Limits of Efficient FFT-based Image Deconvolution

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Kruse, J., Rother, C., & Schmidt, U. (2017). Learning to Push the Limits of Efficient FFT-based Image Deconvolution. In 2017 IEEE International Conference on Computer Vision: ICCV 2017: proceedings: 22-29 October 2017, Venice, Italy (pp. 4596-4604). Piscataway, N.J.: IEEE.


Cite as: http://hdl.handle.net/21.11116/0000-0002-8C53-3
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.