Remove and recover: Deep end-to-end two-stage attention network for single-shot heavy rain removal

Woo Jin Ahn, Tae Koo Kang, Hyun Duck Choi, Myo Taeg Lim

Research output: Contribution to journalArticlepeer-review

Abstract

In this paper, we propose a novel heavy rain removal algorithm using a deep neural network. Unlike most of the existing deraining methods, heavy rain removal is a more challenging task because it is necessary to remove both the rain marks and the haze effects, which are entangled in a complex manner. Motivated by this, we propose a new end-to-end two-stage attention network for single-shot heavy rain removal. The proposed network is connected serially with a removal network and a recovery network, which are based on a newly introduced dilation-wise attention block and skip attention block. Based on these attention techniques, the removal network predicts the heavy rain effect that needs to be removed from a given image, and the recovery network successfully predicts the details that need to be recovered, resulting in a clean image. We also introduce a new realistic RainCityscapes+ dataset, composed of synthesized outdoor images, and demonstrate extensive experiments, the results of which show our approach outperforms the state-of-the-art methods on both real and synthetic datasets quantitatively and qualitatively.

Original languageEnglish
Pages (from-to)216-227
Number of pages12
JournalNeurocomputing
Volume481
DOIs
Publication statusPublished - 2022 Apr 7

Keywords

  • Convolutional neural network
  • Image dehazing
  • Image deraining
  • Image processing

ASJC Scopus subject areas

  • Computer Science Applications
  • Cognitive Neuroscience
  • Artificial Intelligence

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