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    Reflection Removal via Realistic Training Data Generation

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    Name:
    Reflection Removal via Realistic Training Data Generation.pdf
    Size:
    1.508Mb
    Format:
    PDF
    Description:
    Accepted manuscript
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    Type
    Conference Paper
    Authors
    Pang, Youxin
    Yuan, Mengke
    Fu, Qiang cc
    Yan, Dong Ming
    KAUST Department
    Visual Computing Center (VCC)
    Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
    Date
    2020-08-17
    Online Publication Date
    2020-08-17
    Print Publication Date
    2020-08-17
    Permanent link to this record
    http://hdl.handle.net/10754/665577
    
    Metadata
    Show full item record
    Abstract
    We present a valid polarization-based reflection contaminated image synthesis method, which can provide adequate, diverse and authentic training dataset. Meanwhile, we enhance the neural network by introducing the reflection information as guidance and utilizing adaptive convolution kernel size to fuse multi-scale information. We demonstrate that the proposed approach achieves convincing improvements over state of the arts.
    Citation
    Pang, Y., Yuan, M., Fu, Q., & Yan, D.-M. (2020). Reflection Removal via Realistic Training Data Generation. ACM SIGGRAPH 2020 Posters. doi:10.1145/3388770.3407419
    Sponsors
    This work was supported by the National Key R&D Program of China (2019YFB2204104 and 2018YFB2100602). (Portions of) the research in this paper used the 'SIR2' Dataset made available by the ROSE Lab at the Nanyang Technological University, Singapore.
    Publisher
    Association for Computing Machinery (ACM)
    Conference/Event name
    ACM SIGGRAPH 2020 Posters - International Conference on Computer Graphics and Interactive Techniques, SIGGRAPH 2020
    ISBN
    9781450379731
    DOI
    10.1145/3388770.3407419
    Additional Links
    https://dl.acm.org/doi/10.1145/3388770.3407419
    ae974a485f413a2113503eed53cd6c53
    10.1145/3388770.3407419
    Scopus Count
    Collections
    Conference Papers; Visual Computing Center (VCC); Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division

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