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231225s2020 xx |||||o 00| ||eng c |
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|a 10.1109/TPAMI.2019.2915301
|2 doi
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|a eng
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| 100 |
1 |
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|a Shi, Yukai
|e verfasserin
|4 aut
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| 245 |
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|a Face Hallucination by Attentive Sequence Optimization with Reinforcement Learning
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|c 2020
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|a Text
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|a ƒaComputermedien
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|a ƒa Online-Ressource
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|2 rdacarrier
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| 500 |
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|a Date Completed 16.07.2021
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| 500 |
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|a Date Revised 16.07.2021
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|a published: Print-Electronic
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|a Citation Status MEDLINE
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|a Face hallucination is a domain-specific super-resolution problem that aims to generate a high-resolution (HR) face image from a low-resolution (LR) input. In contrast to the existing patch-wise super-resolution models that divide a face image into regular patches and independently apply LR to HR mapping to each patch, we implement deep reinforcement learning and develop a novel attention-aware face hallucination (Attention-FH) framework, which recurrently learns to attend a sequence of patches and performs facial part enhancement by fully exploiting the global interdependency of the image. Specifically, our proposed framework incorporates two components: a recurrent policy network for dynamically specifying a new attended region at each time step based on the status of the super-resolved image and the past attended region sequence, and a local enhancement network for selected patch hallucination and global state updating. The Attention-FH model jointly learns the recurrent policy network and local enhancement network through maximizing a long-term reward that reflects the hallucination result with respect to the whole HR image. Extensive experiments demonstrate that our Attention-FH significantly outperforms the state-of-the-art methods on in-the-wild face images with large pose and illumination variations
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|a Journal Article
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| 650 |
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|a Research Support, Non-U.S. Gov't
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| 700 |
1 |
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|a Li, Guanbin
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Cao, Qingxing
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Wang, Keze
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Lin, Liang
|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 42(2020), 11 vom: 07. Nov., Seite 2809-2824
|w (DE-627)NLM098212257
|x 1939-3539
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|g volume:42
|g year:2020
|g number:11
|g day:07
|g month:11
|g pages:2809-2824
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|u http://dx.doi.org/10.1109/TPAMI.2019.2915301
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