A Categorized Reflection Removal Dataset with Diverse Real-world Scenes
Chenyang Lei, Xuhua Huang, Chenyang Qi, Yankun Zhao, Wenxiu Sun, Qiong, Yan, Qifeng Chen

TL;DR
This paper introduces a large, categorized, real-world reflection removal dataset (CDR) with diverse scenes, enabling better evaluation and development of reflection removal methods, highlighting their strengths and weaknesses across different reflection types.
Contribution
The paper presents a new diverse, categorized real-world reflection removal dataset (CDR) constructed with RAW data, facilitating improved evaluation and development of reflection removal techniques.
Findings
State-of-the-art methods perform well on blurry reflections
Existing methods struggle with certain real-world reflection types
The dataset enables comprehensive analysis of reflection removal techniques
Abstract
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…
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Taxonomy
TopicsImage Enhancement Techniques · Computer Graphics and Visualization Techniques · Image and Signal Denoising Methods
