OpenRR-1k: A Scalable Dataset for Real-World Reflection Removal

TL;DR

This paper introduces OpenRR-1k, a large-scale, high-quality dataset for reflection removal in real-world scenarios, enabling better training and evaluation of reflection removal methods.

Contribution

It presents a novel, scalable data collection paradigm and a new dataset with 1,000 aligned image pairs, addressing the lack of in-the-wild reflection datasets.

Findings

Reflection removal methods improve with the new dataset.

Benchmark results highlight the dataset's effectiveness.

OpenRR-1k enhances robustness in real-world reflection removal.

Abstract

Reflection removal technology plays a crucial role in photography and computer vision applications. However, existing techniques are hindered by the lack of high-quality in-the-wild datasets. In this paper, we propose a novel paradigm for collecting reflection datasets from a fresh perspective. Our approach is convenient, cost-effective, and scalable, while ensuring that the collected data pairs are of high quality, perfectly aligned, and represent natural and diverse scenarios. Following this paradigm, we collect a Real-world, Diverse, and Pixel-aligned dataset (named OpenRR-1k dataset), which contains 1,000 high-quality transmission-reflection image pairs collected in the wild. Through the analysis of several reflection removal methods and benchmark evaluation experiments on our dataset, we demonstrate its effectiveness in improving robustness in challenging real-world environments. Our dataset is available at https://github.com/caijie0620/OpenRR-1k.