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|Title:||Benchmarking single-image reflection removal algorithms||Authors:||Wan, Renjie
Kot, Alex Chichung
|Keywords:||Engineering::Electrical and electronic engineering||Issue Date:||2022||Source:||Wan, R., Shi, B., Li, H., Hong, Y., Duan, L. & Kot, A. C. (2022). Benchmarking single-image reflection removal algorithms. IEEE Transactions On Pattern Analysis and Machine Intelligence, 3168560-. https://dx.doi.org/10.1109/TPAMI.2022.3168560||Journal:||IEEE Transactions on Pattern Analysis and Machine Intelligence||Abstract:||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 '`\sirp'' 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://sir2data.github.io/.||URI:||https://hdl.handle.net/10356/162627||ISSN:||0162-8828||DOI:||10.1109/TPAMI.2022.3168560||Rights:||© 2021 IEEE. All rights reserved.||Fulltext Permission:||none||Fulltext Availability:||No Fulltext|
|Appears in Collections:||EEE Journal Articles|
Updated on Nov 26, 2022
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