# Haze-Aware Attention Network for Single-Image Dehazing
## 4 Experiments
### Comparison with State-of-the-Art Methods  
Results on Synthetic Dataset.We compared our approach with the state-of-the-art on simulated haze images from the RESIDE-Indoor dataset, Haze4K dataset, and RESIDE-Outdoor dataset. For the RESIDE-Indoor dataset, visually, we can observe that the KDDN [51], MSBDN [25], and AOD-Net [1] suffered from texture details loss and color distortion when dealing with small patches of haze (yellow box in Figure 4). They also exhibited edge distortion issues (green box in Figure 4). While DehazeFormer-L [13], Dehamer [52], and FFA-Net [2] produced improved images, they sometimes overly brightened the images, thus leading to the darkening of certain details (yellow box in Figure 4) and showed slight edge distortion (green box in Figure 4). In contrast, our method excelled in preserving details, the clarity of textures, and color authenticity. For the RESIDE-Outdoor dataset, as shown in Figure 5, you can clearly see that AOD-Net [1] and GridDehazeNet [14]had a lot of haze left in the images. The FFA-Net [2] and KDDN [51] both had some small haze leftovers, and DehazeFormer [13] made the images too dark after enhancing them. Our HAA-Net looks the closest to the clean images. When evaluating the performance on the RESIDE-Indoor and Haze4k datasets, our HAA-Net outperformed all other state-of-the-art methods. On the RESIDE-Indoor test set, as shown in Table 1, the HAA-Net achieved the highest PSNR of 41.21 dB and SSIM of 0.996, thus surpassing the second-best method by a 1.16 dB PSNR improvement while also reducing the parameter count by approximately 30%. On the Haze4k dataset, as shown in Table 1, the HAA-Net continued to demonstrate superior performance, thus achieving a PSNR of 33.93 dB and an SSIM of 0.99.  
Real-World Visual Comparisons. We performed real-world haze tests using samples from both the RTTS, NH-HAZE, and Dense-Haze datasets, which are more challenging than synthetic ones. The RTTS dataset includes dense and uneven haze, thereby really testing the robustness and effectiveness of the model. As shown in Figure 2, AOD-Net [1] had a lot of large fog remnants, and both GridDehazeNet [14] and FFA-Net [2] had quite a bit of fog left over, with overenhancement in the sky areas. The MSBDN [25], KDDN [51], and Dehamer [52] all had residual fog, and when the fog was heavy, the enhanced images turned out too dark. DehazeFormer-L [13] also had residual fog and severe texture loss. Clearly, the images restored by our HAA-Net are clear in texture and realistic in color, thus closely matching the clean images. This fully demonstrates the superior robustness and effectiveness of our method. The NH-HAZE is a nonhomogeneous real image dehazing dataset. As shown in Figure 6, by comparing side by side, it is clear that our HAA-Net could perfectly adapt to haze of different concentrations. And our HAA-Net achieved a PSNR of 21.32 dB and an SSIM of 0.692, which is significantly better than other state-of-the-art methods. The Dense-Haze dataset includes images with various extents and densities of haze, which poses a more significant challenge for dehazing. Nonetheless, our HAA-Net method has surpassed the best-performing methods to date, thus obtaining a PSNR of 18.74 dB and an SSIM of 0.620. These achievements strongly validate that our HAA-Net is capable of effectively handling haze of different magnitudes and concentrations.