Dong, Jiangxin ; Roth, Stefan ; Schiele, Bernt (2020)
Deep Wiener Deconvolution: Wiener Meets Deep Learning
for Image Deblurring.
34th Conference on Neural Information Processing Systems (NeurIPS 2020). virtual Conference (06.-12.12.2020)
Konferenzveröffentlichung, Bibliographie
Kurzbeschreibung (Abstract)
We present a simple and effective approach for non-blind image deblurring, combining classical techniques and deep learning. In contrast to existing methods that deblur the image directly in the standard image space, we propose to perform an explicit deconvolution process in a feature space by integrating a classical Wiener deconvolution framework with learned deep features. A multi-scale feature refinement module then predicts the deblurred image from the deconvolved deep features, progressively recovering detail and small-scale structures. The proposed model is trained in an end-to-end manner and evaluated on scenarios with both simulated and real-world image blur. Our extensive experimental results show that the proposed deep Wiener deconvolution network facilitates deblurred results with visibly fewer artifacts. Moreover, our approach quantitatively outperforms state-of-the-art non-blind image deblurring methods by a wide margin.
| Typ des Eintrags: | Konferenzveröffentlichung |
|---|---|
| Erschienen: | 2020 |
| Autor(en): | Dong, Jiangxin ; Roth, Stefan ; Schiele, Bernt |
| Art des Eintrags: | Bibliographie |
| Titel: | Deep Wiener Deconvolution: Wiener Meets Deep Learning for Image Deblurring |
| Sprache: | Englisch |
| Publikationsjahr: | 2020 |
| Buchtitel: | Advances in Neural Information Processing Systems 33 |
| Reihe: | NeurIPS Proceedings |
| Veranstaltungstitel: | 34th Conference on Neural Information Processing Systems (NeurIPS 2020) |
| Veranstaltungsort: | virtual Conference |
| Veranstaltungsdatum: | 06.-12.12.2020 |
| URL / URN: | https://proceedings.neurips.cc/paper/2020 |
| Zugehörige Links: | |
| Kurzbeschreibung (Abstract): | We present a simple and effective approach for non-blind image deblurring, combining classical techniques and deep learning. In contrast to existing methods that deblur the image directly in the standard image space, we propose to perform an explicit deconvolution process in a feature space by integrating a classical Wiener deconvolution framework with learned deep features. A multi-scale feature refinement module then predicts the deblurred image from the deconvolved deep features, progressively recovering detail and small-scale structures. The proposed model is trained in an end-to-end manner and evaluated on scenarios with both simulated and real-world image blur. Our extensive experimental results show that the proposed deep Wiener deconvolution network facilitates deblurred results with visibly fewer artifacts. Moreover, our approach quantitatively outperforms state-of-the-art non-blind image deblurring methods by a wide margin. |
| Fachbereich(e)/-gebiet(e): | 20 Fachbereich Informatik 20 Fachbereich Informatik > Graphisch-Interaktive Systeme 20 Fachbereich Informatik > Visuelle Inferenz |
| Hinterlegungsdatum: | 17 Mär 2021 09:22 |
| Letzte Änderung: | 17 Mär 2021 09:22 |
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