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Deep Wiener Deconvolution: Wiener Meets Deep Learning for Image Deblurring

Dong, Jiangxin ; Roth, Stefan ; Schiele, Bernt (2020):
Deep Wiener Deconvolution: Wiener Meets Deep Learning for Image Deblurring.
In: NeurIPS Proceedings, In: Advances in Neural Information Processing Systems 33,
34th Conference on Neural Information Processing Systems (NeurIPS 2020), virtual Conference, 06.-12.12.2020, [Conference or Workshop Item]

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.

Item Type: Conference or Workshop Item
Erschienen: 2020
Creators: Dong, Jiangxin ; Roth, Stefan ; Schiele, Bernt
Title: Deep Wiener Deconvolution: Wiener Meets Deep Learning for Image Deblurring
Language: English
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.

Title of Book: Advances in Neural Information Processing Systems 33
Series Name: NeurIPS Proceedings
Divisions: 20 Department of Computer Science
20 Department of Computer Science > Interactive Graphics Systems
20 Department of Computer Science > Visual Inference
Event Title: 34th Conference on Neural Information Processing Systems (NeurIPS 2020)
Event Location: virtual Conference
Event Dates: 06.-12.12.2020
Date Deposited: 17 Mar 2021 09:22
Official URL: https://proceedings.neurips.cc/paper/2020
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