Perceiving and Modeling Density for Image Dehazing

Tian Ye, Yunchen Zhang, Mingchao Jiang, Liang Chen, Yun Liu, Sixiang Chen, Erkang Chen ;

"In the real world, the degradation of images taken under haze can be quite complex, where the spatial distribution of haze varies from image to image. Recent methods adopt deep neural networks to recover clean scenes from hazy images directly. However, due to the generic design of network architectures and the failure in estimating an accurate haze degradation model, the generalization ability of recent dehazing methods on real-world hazy images is not ideal. To address the problem of modeling real-world haze degradation, we propose a novel Separable Hybrid Attention (SHA) module to perceive haze density by capturing positional-sensitive features in the orthogonal directions to achieve this goal. Moreover, a density encoding matrix is proposed to model the uneven distribution of the haze explicitly. The density encoding matrix generates positional encoding in a semi-supervised way -- such a haze density perceiving and modeling strategy captures the unevenly distributed degeneration at the feature-level effectively. Through a suitable combination of SHA and density encoding matrix, we design a novel dehazing network architecture, which achieves a good complexity-performance trade-off. Comprehensive evaluation on both synthetic datasets and real-world datasets demonstrates that the proposed method surpasses all the state-of-the-art approaches with a large margin both quantitatively and qualitatively. The code is released in https://github.com/Owen718/ECCV22-Perceiving-and-Modeling-Density-for-Image-Dehazing."