| First Authors | Jacob Kruse |
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| Authors | Jacob Kruse, Carsten Rother, Uwe Schmidt |
| Corresponding Authors | |
| Last Authors | Uwe Schmidt |
| Conference Proceedings Volume Title | 2017 IEEE International Conference on Computer Vision : ICCV 2017 : proceedings : 22-29 October 2017, Venice, Italy |
| Series Title | |
| Conference Name | 2017 IEEE International Conference on Computer Vision : ICCV 2017 |
| Conference Location | Venice, Italy |
| Conference Start Date | 2017-10-22 |
| Conference End Date | 2017-10-29 |
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| Publisher | IEEE |
| Conference Proceedings Editors | |
| ISBN | 978-15386-1032-9 |
| First Page | 4596 |
| Last Page | 4604 |
| Open Access | false |
| Print Publication Date | 2017-10-29 |
| Online Publication Date | |
| 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. |
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| Affiliated With | Myers, Postdocs |
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| Publication Status | Published |
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| DOI | 10.1109/ICCV.2017.491 |
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| Display Publisher Download Only | false |
| Visible On MPI-CBG Website | true |
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| Created By | thuem |
| Added Date | 2018-04-05 |
| Last Edited By | thuem |
| Last Edited Date | 2018-04-05 16:48:33.027 |
| Library ID | 7098 |
| Document ID | WOS:000425498404070 |
| Entry Complete | true |
| eDoc Compliant | true |
| Include in Edoc Report | true |
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| Author Affiliations Complete | false |
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