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231225s2022 xx |||||o 00| ||eng c |
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|a 10.1109/TPAMI.2021.3061604
|2 doi
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|a eng
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| 100 |
1 |
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|a Ren, Wenqi
|e verfasserin
|4 aut
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| 245 |
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|a Deblurring Dynamic Scenes via Spatially Varying Recurrent Neural Networks
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|c 2022
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|a Text
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|a ƒaComputermedien
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|a ƒa Online-Ressource
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|a Date Completed 07.07.2022
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|a Date Revised 09.07.2022
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|a published: Print-Electronic
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|a Citation Status MEDLINE
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|a Deblurring images captured in dynamic scenes is challenging as the motion blurs are spatially varying caused by camera shakes and object movements. In this paper, we propose a spatially varying neural network to deblur dynamic scenes. The proposed model is composed of three deep convolutional neural networks (CNNs) and a recurrent neural network (RNN). The RNN is used as a deconvolution operator on feature maps extracted from the input image by one of the CNNs. Another CNN is used to learn the spatially varying weights for the RNN. As a result, the RNN is spatial-aware and can implicitly model the deblurring process with spatially varying kernels. To better exploit properties of the spatially varying RNN, we develop both one-dimensional and two-dimensional RNNs for deblurring. The third component, based on a CNN, reconstructs the final deblurred feature maps into a restored image. In addition, the whole network is end-to-end trainable. Quantitative and qualitative evaluations on benchmark datasets demonstrate that the proposed method performs favorably against the state-of-the-art deblurring algorithms
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|a Journal Article
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| 650 |
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4 |
|a Research Support, Non-U.S. Gov't
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| 700 |
1 |
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|a Zhang, Jiawei
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Pan, Jinshan
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Liu, Sifei
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Ren, Jimmy S
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Du, Junping
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Cao, Xiaochun
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Yang, Ming-Hsuan
|e verfasserin
|4 aut
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| 773 |
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|i Enthalten in
|t IEEE transactions on pattern analysis and machine intelligence
|d 1979
|g 44(2022), 8 vom: 23. Aug., Seite 3974-3987
|w (DE-627)NLM098212257
|x 1939-3539
|7 nnas
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| 773 |
1 |
8 |
|g volume:44
|g year:2022
|g number:8
|g day:23
|g month:08
|g pages:3974-3987
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| 856 |
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|u http://dx.doi.org/10.1109/TPAMI.2021.3061604
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