Attentive Composite Residual Network for Robust Rain Removal from Single Images

In rainy conditions, imaging devices often capture degraded and blurry images. Most existing works on this problem focus on rain streak removal, but these approaches cannot handle the various types of rain found in images. In this paper, we propose a robust rain removal method for use with single images using an attentive composite residual network. We put forth a single-to-dual encoder-decoder structure, which consists of an attentive net that identifies regions containing rain components during encoding, followed by a dual-channel architecture which recovers the background and detail components of the identified regions during decoding. For the detail subnet, we designed a novel building block, namely a composite residual block (CRB), by constructing multiple residual connections among Res2Net modules. Additionally, we designed another attentive-CRB for the attentive net that uses a squeeze-and-excitation (SE)-Res2Net module, to build a channel-wise attention mechanism. We show that such a deep network can be trained end-to-end from rainy images and that it outperforms the previous state-of-the-art methods on datasets containing different types of rainy images. Experimental results also demonstrate the proposed model's superiority over the competitor's on real rain-affected images, recovering visually clean images and retaining good detail.