CoRRN: Cooperative Reflection Removal Network

Renjie Wan*, Boxin Shi, Haoliang Li, Ling-Yu Duan*, Ah-Hwee Tan, Alex C. Kot

*Corresponding author for this work

Research output: Contribution to journalJournal articlepeer-review

52 Citations (Scopus)


Removing the undesired reflections from images taken through the glass is of broad application to various computer vision tasks. Non-learning based methods utilize different handcrafted priors such as the separable sparse gradients caused by different levels of blurs, which often fail due to their limited description capability to the properties of real-world reflections. In this paper, we propose a network with the feature-sharing strategy to tackle this problem in a cooperative and unified framework, by integrating image context information and the multi-scale gradient information. To remove the strong reflections existed in some local regions, we propose a statistic loss by considering the gradient level statistics between the background and reflections. Our network is trained on a new dataset with 3250 reflection images taken under diverse real-world scenes. Experiments on a public benchmark dataset show that the proposed method performs favorably against state-of-the-art methods.
Original languageEnglish
Pages (from-to)2969-2982
Number of pages14
JournalIEEE Transactions on Pattern Analysis and Machine Intelligence
Issue number12
Early online date10 Jun 2020
Publication statusPublished - Dec 2020

User-Defined Keywords

  • Reflection removal
  • deep learning
  • statistic loss
  • cooperative framework


Dive into the research topics of 'CoRRN: Cooperative Reflection Removal Network'. Together they form a unique fingerprint.

Cite this