Remove and recover: Deep end-to-end two-stage attention network for single-shot heavy rain removal

Woo Jin Ahn, Tae Koo Kang, Hyun Duck Choi, Myo Taeg Lim

    Research output: Contribution to journalArticlepeer-review

    4 Citations (Scopus)

    Abstract

    In this paper, we propose a novel heavy rain removal algorithm using a deep neural network. Unlike most of the existing deraining methods, heavy rain removal is a more challenging task because it is necessary to remove both the rain marks and the haze effects, which are entangled in a complex manner. Motivated by this, we propose a new end-to-end two-stage attention network for single-shot heavy rain removal. The proposed network is connected serially with a removal network and a recovery network, which are based on a newly introduced dilation-wise attention block and skip attention block. Based on these attention techniques, the removal network predicts the heavy rain effect that needs to be removed from a given image, and the recovery network successfully predicts the details that need to be recovered, resulting in a clean image. We also introduce a new realistic RainCityscapes+ dataset, composed of synthesized outdoor images, and demonstrate extensive experiments, the results of which show our approach outperforms the state-of-the-art methods on both real and synthetic datasets quantitatively and qualitatively.

    Original languageEnglish
    Pages (from-to)216-227
    Number of pages12
    JournalNeurocomputing
    Volume481
    DOIs
    Publication statusPublished - 2022 Apr 7

    Bibliographical note

    Funding Information:
    This work was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (NRF-2016R1D1A1B01016071, ICT (NRF-2016R1D1A1B01016071 and NRF-2020R1G1A1101070).

    Publisher Copyright:
    © 2022 Elsevier B.V.

    Keywords

    • Convolutional neural network
    • Image dehazing
    • Image deraining
    • Image processing

    ASJC Scopus subject areas

    • Computer Science Applications
    • Cognitive Neuroscience
    • Artificial Intelligence

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