A Categorized Reflection Removal Dataset with Diverse Real-world Scenes
This addresses a data bottleneck for researchers in computer vision working on reflection removal, though it is incremental as it builds on existing datasets.
The authors tackled the lack of a diverse real-world dataset for reflection removal by constructing a categorized, diverse, and real-world (CDR) dataset, showing that state-of-the-art methods perform well on blurry reflections but fail on other types.
Due to the lack of a large-scale reflection removal dataset with diverse real-world scenes, many existing reflection removal methods are trained on synthetic data plus a small amount of real-world data, which makes it difficult to evaluate the strengths or weaknesses of different reflection removal methods thoroughly. Furthermore, existing real-world benchmarks and datasets do not categorize image data based on the types and appearances of reflection (e.g., smoothness, intensity), making it hard to analyze reflection removal methods. Hence, we construct a new reflection removal dataset that is categorized, diverse, and real-world (CDR). A pipeline based on RAW data is used to capture perfectly aligned input images and transmission images. The dataset is constructed using diverse glass types under various environments to ensure diversity. By analyzing several reflection removal methods and conducting extensive experiments on our dataset, we show that state-of-the-art reflection removal methods generally perform well on blurry reflection but fail in obtaining satisfying performance on other types of real-world reflection. We believe our dataset can help develop novel methods to remove real-world reflection better. Our dataset is available at https://alexzhao-hugga.github.io/Real-World-Reflection-Removal/.