Exploiting Global and Local Attentions for Heavy Rain Removal on Single Images

• URL: http://arxiv.org/abs/2104.08126v1
• Date: Fri, 16 Apr 2021 14:08:27 GMT
• Title: Exploiting Global and Local Attentions for Heavy Rain Removal on Single Images
• Authors: Dac Tung Vu, Juan Luis Gonzalez, Munchurl Kim
• Abstract summary: Heavy rain removal from a single image is the task of simultaneously eliminating rain streaks and fog. Most existing rain removal methods do not generalize well for the heavy rain case. We propose a novel network architecture consisting of three sub-networks to remove heavy rain from a single image.
• Score: 35.596659286313766
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Heavy rain removal from a single image is the task of simultaneously eliminating rain streaks and fog, which can dramatically degrade the quality of captured images. Most existing rain removal methods do not generalize well for the heavy rain case. In this work, we propose a novel network architecture consisting of three sub-networks to remove heavy rain from a single image without estimating rain streaks and fog separately. The first sub-net, a U-net-based architecture that incorporates our Spatial Channel Attention (SCA) blocks, extracts global features that provide sufficient contextual information needed to remove atmospheric distortions caused by rain and fog. The second sub-net learns the additive residues information, which is useful in removing rain streak artifacts via our proposed Residual Inception Modules (RIM). The third sub-net, the multiplicative sub-net, adopts our Channel-attentive Inception Modules (CIM) and learns the essential brighter local features which are not effectively extracted in the SCA and additive sub-nets by modulating the local pixel intensities in the derained images. Our three clean image results are then combined via an attentive blending block to generate the final clean image. Our method with SCA, RIM, and CIM significantly outperforms the previous state-of-the-art single-image deraining methods on the synthetic datasets, shows considerably cleaner and sharper derained estimates on the real image datasets. We present extensive experiments and ablation studies supporting each of our method's contributions on both synthetic and real image datasets.

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