More Realistic Edges, Textures, and Colors for Image Non-Homogeneous Dehazing

The existing image dehazing algorithms perform suboptimal in non-homogeneous and/or dense haze scenarios. The loss of feature information and alteration of color distribution cause images to deviate from real-world scenes when haze suppresses image details. To address these issues, we design a dual-branch non-homogeneous dehazing network integrating discrete wavelet transform (DWT), multi-scale feature fusion, and color constraints to achieve dehazed images with more realistic edges, textures, and colors. Specifically, we first introduce DWT into a multi-scale encoder–decoder network structure to capture more details and edge information. Then, a feature supplement and enhancement module (FSEM) combining features from hazy images at different scales and features from the previous stage is devised to enhance the multi-scale feature capture capability of rich textures in complex scenes. Finally, we propose a pixel-wise color consistency loss that combines pixel similarity and angular difference to constrain the dehazed images to closely match the color distribution of clear images. Experimental results indicate that the proposed dehazing network outperforms the state-of-the-art non-homogeneous dehazing methods on relevant public benchmarks and has more realistic edges, textures, and colors.