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|a 10.1109/TPAMI.2022.3168560
|2 doi
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|a eng
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| 100 |
1 |
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|a Wan, Renjie
|e verfasserin
|4 aut
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| 245 |
1 |
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|a Benchmarking Single-Image Reflection Removal Algorithms
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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 Completed 06.04.2023
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|a Date Revised 06.04.2023
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|a published: Print-Electronic
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|a Citation Status PubMed-not-MEDLINE
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| 520 |
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|a Reflection removal has been discussed for more than decades. This paper aims to provide the analysis for different reflection properties and factors that influence image formation, an up-to-date taxonomy for existing methods, a benchmark dataset, and the unified benchmarking evaluations for state-of-the-art (especially learning-based) methods. Specifically, this paper presents a SIngle-image Reflection Removal Plus dataset "SIR 2+ " with the new consideration for in-the-wild scenarios and glass with diverse color and unplanar shapes. We further perform quantitative and visual quality comparisons for state-of-the-art single-image reflection removal algorithms. Open problems for improving reflection removal algorithms are discussed at the end. Our dataset and follow-up update can be found at https://reflectionremoval.github.io/sir2data/
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|a Journal Article
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1 |
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|a Shi, Boxin
|e verfasserin
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| 700 |
1 |
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|a Li, Haoliang
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Hong, Yuchen
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Duan, Ling-Yu
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Kot, Alex C
|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 45(2023), 2 vom: 19. Feb., Seite 1424-1441
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|g year:2023
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|g day:19
|g month:02
|g pages:1424-1441
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|u http://dx.doi.org/10.1109/TPAMI.2022.3168560
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