Benchmarking single-image reflection removal algorithms

Renjie Wan, Boxin Shi, Haoliang Li, Yuchen Hong, Ling-Yu Duan, Alex C. Kot

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 aSIngle-image Reflection Removal Plus dataset “SIR2+ ” 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/.

External IDs:doi:10.1109/tpami.2022.3168560