Benchmarking single-image reflection removal algorithms

Renjie Wan, Boxin Shi*, Haoliang Li, Yuchen Hong, Lingyu Duan, Alex C. Kot

*Corresponding author for this work

Research output: Contribution to journalJournal articlepeer-review

4 Citations (Scopus)


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 “SIR2+ ” 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
Pages (from-to)1424 - 1441
Number of pages18
JournalIEEE Transactions on Pattern Analysis and Machine Intelligence
Issue number2
Early online date19 Apr 2022
Publication statusPublished - 1 Feb 2023

Scopus Subject Areas

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

User-Defined Keywords

  • Reflection removal
  • benchmark dataset
  • deep learning


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