Recurrent Squeeze-and-Excitation Context Aggregation Net for Single Image Deraining

TL;DR

This paper introduces a novel deep recurrent neural network architecture that effectively removes rain streaks from images by leveraging large receptive fields, multi-stage processing, and contextual information, outperforming existing methods.

Contribution

The proposed RESCAN network combines dilated convolutions, squeeze-and-excitation blocks, and recurrent stages to improve single image deraining performance.

Findings

Outperforms state-of-the-art deraining methods on synthetic datasets

Effective in removing diverse rain streaks in real-world images

Utilizes multi-stage recurrent processing for better rain layer separation

Abstract

Rain streaks can severely degrade the visibility, which causes many current computer vision algorithms fail to work. So it is necessary to remove the rain from images. We propose a novel deep network architecture based on deep convolutional and recurrent neural networks for single image deraining. As contextual information is very important for rain removal, we first adopt the dilated convolutional neural network to acquire large receptive field. To better fit the rain removal task, we also modify the network. In heavy rain, rain streaks have various directions and shapes, which can be regarded as the accumulation of multiple rain streak layers. We assign different alpha-values to various rain streak layers according to the intensity and transparency by incorporating the squeeze-and-excitation block. Since rain streak layers overlap with each other, it is not easy to remove the rain in one stage. So we further decompose the rain removal into multiple stages. Recurrent neural network is incorporated to preserve the useful information in previous stages and benefit the rain removal in later stages. We conduct extensive experiments on both synthetic and real-world datasets. Our proposed method outperforms the state-of-the-art approaches under all evaluation metrics. Codes and supplementary material are available at our project webpage: https://xialipku.github.io/RESCAN .