Publication Type
Journal Article
Version
acceptedVersion
Publication Date
6-2019
Abstract
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.
Keywords
Reflection removal, deep learning, statistic loss, cooperative framework
Discipline
Programming Languages and Compilers | Software Engineering
Research Areas
Data Science and Engineering
Publication
IEEE Transactions on Pattern Analysis and Machine Intelligence
First Page
1
Last Page
14
ISSN
0162-8828
Identifier
10.1109/TPAMI.2019.2921574
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Citation
WEN, Renjie; SHI, Boxin; LI, Haoliang; DUAN, Ling-Yu; TAN, Ah-hwee; and KOT, Alex C..
CoRRN: Cooperative Reflection Removal Network. (2019). IEEE Transactions on Pattern Analysis and Machine Intelligence. 1-14.
Available at: https://ink.library.smu.edu.sg/sis_research/5178
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
https://doi.org/10.1109/TPAMI.2019.2921574