Generative Adversarial and Self-Supervised Dehazing Network

Shengdong Zhang, Xiaoqin Zhang, Shaohua Wan, Wenqi Ren, Liping Zhao, Linlin Shen

Research output: Journal PublicationArticlepeer-review

5 Citations (Scopus)

Abstract

Owing to the fast developments of economics, a lot of devices and objects have been connected and have formed the Internet of Things (IoT). Visual sensors have been applied in vehicle navigation, traffic situational awareness, and traffic safety management. However, the particles in the air degrade the imaging quality, which affects the performance of vehicle navigation, traffic situational awareness, and traffic safety management. Deep-learning-based dehazing methods were proposed to address this issue. However, these methods are trained with simulated hazy images and cannot generalize to natural haze images well. To address the domain shift problem, some methods resort to zero-shot learning or domain adaption to boost the generalization of the model on natural haze images. However, the relevance between dehazed results and clean images is ignored by zero-shot dehazing methods. Domain-adaption-based dehazing methods ignore the relationship between the dehazed results and the hazy images. To overcome these issues, a generative adversarial and self-supervised dehazing network is introduced to boost the dehazing performance on real haze images. First, generative adversarial is employed to construct the relevance between dehazed results and haze-free images, which can boost the natural appearance of dehazed results. Second, self-supervised learning is employed to construct the relevance between the dehazed results and hazy images, which can restrict the solution space of dehazing. To show the effectiveness of the proposed model, we conduct extensive experiments on real and simulated haze images. Compared with state-of-the-art methods, the proposed model achieves state-of-the-art dehazing performance.

Original languageEnglish
Pages (from-to)4187-4197
Number of pages11
JournalIEEE Transactions on Industrial Informatics
Volume20
Issue number3
DOIs
Publication statusPublished - 1 Mar 2024
Externally publishedYes

Keywords

  • Dehazing
  • domain shift
  • generative adversarial
  • natural haze images
  • self-supervised
  • visual Internet of Things (IoT)

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Information Systems
  • Computer Science Applications
  • Electrical and Electronic Engineering

Fingerprint

Dive into the research topics of 'Generative Adversarial and Self-Supervised Dehazing Network'. Together they form a unique fingerprint.

Cite this