Multiscale Depth Fusion With Contextual Hybrid Enhancement Network for Image Dehazing

Image dehazing is a research focus, however the existing methods do not make enough use of depth information, which leads to poor dehazing effect for large depth scenes. Therefore, according to the relevance between the difference of haze concentration and depth in different regions of the image, it has become a challenging task. To address this challenge, firstly, we propose a novel multiscale depth information fusion enhancement network to improve dehazing ability in scenes with large depth changes. Secondly, our method hierarchically fuses the multiscale features of the depth map with the hazy image. We built a grade-by-grade fusion structure to fuse and enhance contextual features with attention mechanism. Finally, to make our model better adapt to real hazy images, restore the image while maintaining details. We add local prior knowledge supervision to each branch. We compare our model with other state-of-the-art methods on hazy datasets. The experimental results show that our dehazing model not only achieve optimal performance on synthetic images but also show certain advantages in real-world hazy images.