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231226s2023 xx |||||o 00| ||eng c |
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|a 10.1109/TIP.2023.3312912
|2 doi
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|e rakwb
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|a eng
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| 100 |
1 |
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|a Wu, Qiucheng
|e verfasserin
|4 aut
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| 245 |
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|a Broad Spectrum Image Deblurring via an Adaptive Super-Network
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|c 2023
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|a Text
|b txt
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|a ƒaComputermedien
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|a ƒa Online-Ressource
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|a Date Revised 22.09.2023
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|a published: Print-Electronic
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|a Citation Status PubMed-not-MEDLINE
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|a In blurry images, the degree of image blurs may vary drastically due to different factors, such as varying speeds of shaking cameras and moving objects, as well as defects of the camera lens. However, current end-to-end models failed to explicitly take into account such diversity of blurs. This unawareness compromises the specialization at each blur level, yielding sub-optimal deblurred images as well as redundant post-processing. Therefore, how to specialize one model simultaneously at different blur levels, while still ensuring coverage and generalization, becomes an emerging challenge. In this work, we propose Ada-Deblur, a super-network that can be applied to a "broad spectrum" of blur levels with no re-training on novel blurs. To balance between individual blur level specialization and wide-range blur levels coverage, the key idea is to dynamically adapt the network architectures from a single well-trained super-network structure, targeting flexible image processing with different deblurring capacities at test time. Extensive experiments demonstrate that our work outperforms strong baselines by demonstrating better reconstruction accuracy while incurring minimal computational overhead. Besides, we show that our method is effective for both synthetic and realistic blurs compared to these baselines. The performance gap between our model and the state-of-the-art becomes more prominent when testing with unseen and strong blur levels. Specifically, our model demonstrates surprising deblurring performance on these images with PSNR improvements of around 1 dB. Our code is publicly available at https://github.com/wuqiuche/Ada-Deblur
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|a Journal Article
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| 700 |
1 |
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|a Jiang, Yifan
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Wu, Junru
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Kulikov, Victor
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Goel, Vidit
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Orlov, Nikita
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Shi, Humphrey
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Wang, Zhangyang
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Chang, Shiyu
|e verfasserin
|4 aut
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| 773 |
0 |
8 |
|i Enthalten in
|t IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
|d 1992
|g 32(2023) vom: 18., Seite 5270-5282
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|x 1941-0042
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|g volume:32
|g year:2023
|g day:18
|g pages:5270-5282
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|u http://dx.doi.org/10.1109/TIP.2023.3312912
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