Image Dehazing with Contextualized Attentive U-NET

Yean Wei Lee, Lai Kuan Wong, John See

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Haze, which occurs due to the accumulation of fine dust or smoke particles in the atmosphere, degrades outdoor imaging, resulting in reduced attractiveness of outdoor photography and the effectiveness of vision-based systems. In this paper, we present an end-to-end convolutional neural network for image dehazing. Our proposed U-Net based architecture employs Squeeze-and-Excitation (SE) blocks at the skip connections to enforce channel-wise attention and parallelized dilated convolution blocks at the bottleneck to capture both local and global context, resulting in a richer representation of the image features. Experimental results demonstrate the effectiveness of the proposed method in achieving state-of-the-art performance on the benchmark SOTS dataset.

Original languageEnglish
Title of host publication2020 IEEE International Conference on Image Processing, ICIP 2020 - Proceedings
PublisherIEEE
Pages1068-1072
Number of pages5
ISBN (Electronic)9781728163956
DOIs
Publication statusPublished - Oct 2020
Event2020 IEEE International Conference on Image Processing - Virtual, Abu Dhabi, United Arab Emirates
Duration: 25 Oct 202028 Oct 2020

Conference

Conference2020 IEEE International Conference on Image Processing
Abbreviated titleICIP 2020
Country/TerritoryUnited Arab Emirates
CityVirtual, Abu Dhabi
Period25/10/2028/10/20

Keywords

  • CNN
  • deep neural network
  • dilated convolution
  • Image dehazing
  • U-Net

ASJC Scopus subject areas

  • Software
  • Computer Vision and Pattern Recognition
  • Signal Processing

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