Haze generation and feature fusion network aiming at real-world single image dehazing
Year: 2024
Type: conference-paper
Abstract: Because most dehazing methods use synthesized haze images for training, they may perform well on synthesized datasets. However, when these methods are applied to real-world scenes, their performance may significantly decrease due to domain shift. Therefore, we propose a dehazing network for real-world hazy scenes. This network includes a haze generation network that can utilize the hazy information of real haze images to generate images that are closer to real hazy scenes, generating training pairs to address the domain shift problem. The network also includes a dehazing network that integrates feature fusion attention mechanisms, which can achieve better dehazing performance.
Authors: Hao Pan, Huanle Tang
Institution: Wuhan University of Technology
Language: English
Topic: Image Enhancement Techniques
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W4401521406