Single-image rain removal using residual deep learning

Takuro Matsui, Takanori Fujisawa, Takuro Yamaguchi, Masaaki Ikehara

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    3 Citations (Scopus)

    Abstract

    Most outdoor vision systems can be influenced by rainy weather conditions. In this paper, we address a rain removal problem from a single image. Some existing de-raining methods suffer from hue change due to neglect of the information in low frequency layer. Others fail in assuming enough rainy image models. To solve them, we propose a residual deep network architecture called ResDerainNet. Based on the deep convolutional neural network (CNN), we learn the mapping relationship between rainy and residual images from data. Furthermore, for training, we synthesize rainy images considering various rain models. Specifically, we mainly focus on the composite models as well as orientations and scales of rain streaks. The experiments demonstrate that our proposed model is applicable to a variety of images. Compared with state-of-the-art methods, our proposed method achieves better results on both synthetic and real-world images.

    Original languageEnglish
    Title of host publication2018 IEEE International Conference on Image Processing, ICIP 2018 - Proceedings
    PublisherIEEE Computer Society
    Pages3928-3932
    Number of pages5
    ISBN (Electronic)9781479970612
    DOIs
    Publication statusPublished - 2018 Aug 29
    Event25th IEEE International Conference on Image Processing, ICIP 2018 - Athens, Greece
    Duration: 2018 Oct 72018 Oct 10

    Publication series

    NameProceedings - International Conference on Image Processing, ICIP
    ISSN (Print)1522-4880

    Conference

    Conference25th IEEE International Conference on Image Processing, ICIP 2018
    CountryGreece
    CityAthens
    Period18/10/718/10/10

    Keywords

    • Batch normalization
    • Convolutional neural networks
    • Deep learning
    • Rain removal
    • Residual learning

    ASJC Scopus subject areas

    • Software
    • Computer Vision and Pattern Recognition
    • Signal Processing

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  • Cite this

    Matsui, T., Fujisawa, T., Yamaguchi, T., & Ikehara, M. (2018). Single-image rain removal using residual deep learning. In 2018 IEEE International Conference on Image Processing, ICIP 2018 - Proceedings (pp. 3928-3932). [8451612] (Proceedings - International Conference on Image Processing, ICIP). IEEE Computer Society. https://doi.org/10.1109/ICIP.2018.8451612