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Heavy rain removal from a single image is the task of simultaneouslyeliminating rain streaks and fog, which can dramatically degrade the quality ofcaptured images. Most existing rain removal methods do not generalize well forthe heavy rain case. In this work, we propose a novel network architectureconsisting of three sub-networks to remove heavy rain from a single imagewithout estimating rain streaks and fog separately. The first sub-net, aU-net-based architecture that incorporates our Spatial Channel Attention (SCA)blocks, extracts global features that provide sufficient contextual informationneeded to remove atmospheric distortions caused by rain and fog. The secondsub-net learns the additive residues information, which is useful in removingrain streak artifacts via our proposed Residual Inception Modules (RIM). Thethird sub-net, the multiplicative sub-net, adopts our Channel-attentiveInception Modules (CIM) and learns the essential brighter local features whichare not effectively extracted in the SCA and additive sub-nets by modulatingthe local pixel intensities in the derained images. Our three clean imageresults are then combined via an attentive blending block to generate the finalclean image. Our method with SCA, RIM, and CIM significantly outperforms theprevious state-of-the-art single-image deraining methods on the syntheticdatasets, shows considerably cleaner and sharper derained estimates on the realimage datasets. We present extensive experiments and ablation studiessupporting each of our method's contributions on both synthetic and real imagedatasets.

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