Dehazing for Real-World Scenarios through Adaptive Perturbation Injection and Local-Global Dual-Stage Defense

Although existing dehazing models have achieved success on synthetic hazy images, they still face significant challenges when dealing with real-world hazy images. To address this, we propose an adaptive perturbation injection and dual-layer defense method for real-scene image dehazing, composed of a perturbation generation module, a local-global dual-layer defense module, and a semantic guidance module.

The perturbation generation module adaptively generates perturbation cues to simulate the degradation factors during the formation of real-scene hazy images, and injects these perturbations into synthetic-domain hazy images to reduce the distribution gap between synthetic and real-domain hazy images. The local-global defense module resists perturbation information from both local and global perspectives, enhancing the dehazing performance of the model in real scenarios.