Non-local Channel Aggregation Network for Single Image Rain Removal

• URL: http://arxiv.org/abs/2103.02488v1
• Date: Wed, 3 Mar 2021 15:57:37 GMT
• Title: Non-local Channel Aggregation Network for Single Image Rain Removal
• Authors: Zhipeng Su, Yixiong Zhang, Xiao-Ping Zhang, Feng Qi
• Abstract summary: We propose a non-local channel aggregation network (NCANet) to address the single image rain removal problem. NCANet models 2D rainy images as sequences of vectors in three directions, namely vertical direction, transverse direction and channel direction. Recurrently aggregating information from all three directions enables our model to capture the long-range dependencies in both channels and spaitials locations.
• Score: 3.7679182997120066
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Rain streaks showing in images or videos would severely degrade the performance of computer vision applications. Thus, it is of vital importance to remove rain streaks and facilitate our vision systems. While recent convolutinal neural network based methods have shown promising results in single image rain removal (SIRR), they fail to effectively capture long-range location dependencies or aggregate convolutional channel information simultaneously. However, as SIRR is a highly illposed problem, these spatial and channel information are very important clues to solve SIRR. First, spatial information could help our model to understand the image context by gathering long-range dependency location information hidden in the image. Second, aggregating channels could help our model to concentrate on channels more related to image background instead of rain streaks. In this paper, we propose a non-local channel aggregation network (NCANet) to address the SIRR problem. NCANet models 2D rainy images as sequences of vectors in three directions, namely vertical direction, transverse direction and channel direction. Recurrently aggregating information from all three directions enables our model to capture the long-range dependencies in both channels and spaitials locations. Extensive experiments on both heavy and light rain image data sets demonstrate the effectiveness of the proposed NCANet model.

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