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 a SIngle-image Reflection Removal Plus dataset “SIR <inline-formula><tex-math notation="LaTeX">$^{2+}$</tex-math></inline-formula> ” 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 <uri>https://reflectionremoval.github.io/sir2data/</uri> .