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240530s2024 xx |||||o 00| ||eng c |
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|a 10.1109/TPAMI.2024.3406821
|2 doi
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|a eng
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| 100 |
1 |
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|a Dai, Yuekun
|e verfasserin
|4 aut
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| 245 |
1 |
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|a Flare7K++
|b Mixing Synthetic and Real Datasets for Nighttime Flare Removal and Beyond
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|c 2024
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|a Text
|b txt
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|a ƒaComputermedien
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|a Date Revised 03.10.2024
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|a published: Print-Electronic
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|a Citation Status PubMed-not-MEDLINE
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|a Artificial lights commonly leave strong lens flare artifacts on the images captured at night, degrading both the visual quality and performance of vision algorithms. Existing flare removal approaches mainly focus on removing daytime flares and fail in nighttime cases. Nighttime flare removal is challenging due to the unique luminance and spectrum of artificial lights, as well as the diverse patterns and image degradation of the flares. The scarcity of the nighttime flare removal dataset constrains the research on this crucial task. In this paper, we introduce Flare7K++, the first comprehensive nighttime flare removal dataset, consisting of 962 real-captured flare images (Flare-R) and 7000 synthetic flares (Flare7K). Compared to Flare7K, Flare7K++ is particularly effective in eliminating complicated degradation around the light source, which is intractable by using synthetic flares alone. Besides, the previous flare removal pipeline relies on the manual threshold and blur kernel settings to extract light sources, which may fail when the light sources are tiny or not overexposed. To address this issue, we additionally provide the annotations of light sources in Flare7K++ and propose a new end-to-end pipeline to preserve the light source while removing lens flares. Our dataset and pipeline offer a valuable foundation and benchmark for future investigations into nighttime flare removal studies. Extensive experiments demonstrate that Flare7K++ supplements the diversity of existing flare datasets and pushes the frontier of nighttime flare removal toward real-world scenarios
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|a Journal Article
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|a Li, Chongyi
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Zhou, Shangchen
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Feng, Ruicheng
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Luo, Yihang
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Loy, Chen Change
|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 46(2024), 11 vom: 29. Okt., Seite 7041-7055
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|x 1939-3539
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|g volume:46
|g year:2024
|g number:11
|g day:29
|g month:10
|g pages:7041-7055
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| 856 |
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|u http://dx.doi.org/10.1109/TPAMI.2024.3406821
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