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20240229162828.0 |
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231225s2020 xx |||||o 00| ||eng c |
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|a 10.1109/TIP.2020.2990354
|2 doi
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|a pubmed24n1308.xml
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|a (DE-627)NLM309486068
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|a DE-627
|b ger
|c DE-627
|e rakwb
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|a eng
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| 100 |
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|a Yasarla, Rajeev
|e verfasserin
|4 aut
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| 245 |
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|a Deblurring Face Images using Uncertainty Guided Multi-Stream Semantic Networks
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|c 2020
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|a Text
|b txt
|2 rdacontent
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|a ƒaComputermedien
|b c
|2 rdamedia
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|a ƒa Online-Ressource
|b cr
|2 rdacarrier
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|a Date Revised 27.02.2024
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|a published: Print-Electronic
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|a Citation Status Publisher
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|a We propose a novel multi-stream architecture and training methodology that exploits semantic labels for facial image deblurring. The proposed Uncertainty Guided Multi-Stream Semantic Network (UMSN) processes regions belonging to each semantic class independently and learns to combine their outputs into the final deblurred result. Pixel-wise semantic labels are obtained using a segmentation network. A predicted confidence measure is used during training to guide the network towards the challenging regions of the human face such as the eyes and nose. The entire network is trained in an end-to-end fashion. Comprehensive experiments on three different face datasets demonstrate that the proposed method achieves significant improvements over the recent state-of-the-art face deblurring methods. Code is available at
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|a Journal Article
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|a Perazzi, Federico
|e verfasserin
|4 aut
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| 700 |
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|a Patel, Vishal M
|e verfasserin
|4 aut
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| 773 |
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|i Enthalten in
|t IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
|d 1992
|g (2020) vom: 30. Apr.
|w (DE-627)NLM09821456X
|x 1941-0042
|7 nnns
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| 773 |
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|g year:2020
|g day:30
|g month:04
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|u http://dx.doi.org/10.1109/TIP.2020.2990354
|3 Volltext
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