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231225s2021 xx |||||o 00| ||eng c |
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|a 10.1109/TIP.2021.3058764
|2 doi
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|a pubmed25n1071.xml
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|a (DE-627)NLM321591364
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|a (NLM)33600315
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|a DE-627
|b ger
|c DE-627
|e rakwb
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| 041 |
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|a eng
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| 100 |
1 |
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|a Deng, Xin
|e verfasserin
|4 aut
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| 245 |
1 |
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|a Deep Coupled Feedback Network for Joint Exposure Fusion and Image Super-Resolution
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|c 2021
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|a Text
|b txt
|2 rdacontent
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|a ƒaComputermedien
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|2 rdamedia
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|a ƒa Online-Ressource
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|a Date Completed 25.02.2021
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|a Date Revised 25.02.2021
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|a published: Print-Electronic
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|a Citation Status PubMed-not-MEDLINE
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|a Nowadays, people are getting used to taking photos to record their daily life, however, the photos are actually not consistent with the real natural scenes. The two main differences are that the photos tend to have low dynamic range (LDR) and low resolution (LR), due to the inherent imaging limitations of cameras. The multi-exposure image fusion (MEF) and image super-resolution (SR) are two widely-used techniques to address these two issues. However, they are usually treated as independent researches. In this paper, we propose a deep Coupled Feedback Network (CF-Net) to achieve MEF and SR simultaneously. Given a pair of extremely over-exposed and under-exposed LDR images with low-resolution, our CF-Net is able to generate an image with both high dynamic range (HDR) and high-resolution. Specifically, the CF-Net is composed of two coupled recursive sub-networks, with LR over-exposed and under-exposed images as inputs, respectively. Each sub-network consists of one feature extraction block (FEB), one super-resolution block (SRB) and several coupled feedback blocks (CFB). The FEB and SRB are to extract high-level features from the input LDR image, which are required to be helpful for resolution enhancement. The CFB is arranged after SRB, and its role is to absorb the learned features from the SRBs of the two sub-networks, so that it can produce a high-resolution HDR image. We have a series of CFBs in order to progressively refine the fused high-resolution HDR image. Extensive experimental results show that our CF-Net drastically outperforms other state-of-the-art methods in terms of both SR accuracy and fusion performance. The software code is available here https://github.com/ytZhang99/CF-Net
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|a Journal Article
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1 |
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|a Zhang, Yutong
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Xu, Mai
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Gu, Shuhang
|e verfasserin
|4 aut
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| 700 |
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|a Duan, Yiping
|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 30(2021) vom: 18., Seite 3098-3112
|w (DE-627)NLM09821456X
|x 1941-0042
|7 nnas
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| 773 |
1 |
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|g volume:30
|g year:2021
|g day:18
|g pages:3098-3112
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| 856 |
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|u http://dx.doi.org/10.1109/TIP.2021.3058764
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|d 30
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