  Image dehazing poses significant challenges in environmental perception.
Recent research mainly focus on deep learning-based methods with single
modality, while they may result in severe information loss especially in
dense-haze scenarios. The infrared image exhibits robustness to the haze,
however, existing methods have primarily treated the infrared modality as
auxiliary information, failing to fully explore its rich information in
dehazing. To address this challenge, the key insight of this study is to design
a visible-infrared fusion network for image dehazing. In particular, we propose
a multi-scale Deep Structure Feature Extraction (DSFE) module, which
incorporates the Channel-Pixel Attention Block (CPAB) to restore more spatial
and marginal information within the deep structural features. Additionally, we
introduce an inconsistency weighted fusion strategy to merge the two modalities
by leveraging the more reliable information. To validate this, we construct a
visible-infrared multimodal dataset called AirSim-VID based on the AirSim
simulation platform. Extensive experiments performed on challenging real and
simulated image datasets demonstrate that VIFNet can outperform many
state-of-the-art competing methods. The code and dataset are available at
https://github.com/mengyu212/VIFNet_dehazing.
