Benchmarking single-image reflection removal algorithms

• المؤلفون الرئيسيون: Wan, Renjie, Shi, Boxin, Li, Haoliang, Hong, Yuchen, Duan, Ling-Yu, Kot, Alex Chichung
• مؤلفون آخرون: School of Electrical and Electronic Engineering
• التنسيق: مقال
• اللغة: English
• منشور في: 2022
• الموضوعات: Engineering::Electrical and electronic engineering; Reflection Removal; Benchmark Dataset
• الوصول للمادة أونلاين: https://hdl.handle.net/10356/162627

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/.