Benchmarking single-image reflection removal algorithms

Renjie Wan, Boxin Shi, Haoliang Li, Yuchen Hong, Lingyu Duan, Alex Kot Chichung

Research output: Contribution to journalArticlepeer-review


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

Original languageEnglish
JournalIEEE Transactions on Pattern Analysis and Machine Intelligence
Publication statusE-pub ahead of print - 19 Apr 2022

Scopus Subject Areas

  • Software
  • Computer Vision and Pattern Recognition
  • Computational Theory and Mathematics
  • Artificial Intelligence
  • Applied Mathematics

User-Defined Keywords

  • benchmark dataset
  • Benchmark testing
  • Cameras
  • Deep learning
  • deep learning
  • Glass
  • Mathematical models
  • Reflection
  • Reflection removal
  • Reflectivity


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