## 4 Experiments
### Comparison with Other Methods
#### 4.3.2 Visual Comparisons  
Figures 9 and 10 show the visual comparison of CCD-Net with five other methods on the RESIDE-indoor and RESIDE-6K datasets. On the RESIDE-indoor dataset, DCP causes the image to be darker in tone, with a more pronounced color cast. GridDehazeNet does not fully recover the natural tone and sharpness of the GT image, despite the higher contrast. DehazeFormer-T recovers details that are more blurred, such as the texture of the desktop and the background. D4 and ODCR result in poorly defined image edges and loss of details.  
As shown in Figure 9, our method effectively restores edge details, producing a sharper door outline and preserving the fine structure of the window shutters. In contrast, the DCP method introduces a noticeable halo effect, causing severe edge artifacts, while GridDehazeNet struggles with blurriness, failing to accurately reconstruct the texture. In terms of color reproduction, our method also performs well in reproducing the warm tones of the ceiling light, and the colors of the walls and furniture are natural and close to the original scene, avoiding the problems of yellowish, dark, or skewed colors that occur in DCP and GridDehazeNet, and especially in the reproduction of the window light and the white areas of the walls, which is much closer to the real distribution of the colors. In contrast, the D4 and ODCR methods are slightly less bright and saturated, and fail to show the ideal color effect.  
Figure 8: Radar chart of comparative experimental results.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{3}{c}{**RESIDE-Indoor**} & \multicolumn{3}{c}{**RESIDE-6K**} & \multicolumn{3}{c}{**Overhead**} \\ \cline{2-9}  & **PSNR** & **CISIM** & **CIEDE** & **PSNR** & **CISIM** & **CIEDE** & **Param** & **Inference Time (ms)** \\ \hline DCP [11] & 16.20 & 0.818 & 14.86 & 17.88 & 0.816 & 14.03 & - & - \\ GridDehazeNet [50] & 32.16 & 0.984 & 6.17 & 25.86 & 0.944 & 9.28 & 0.956 & 2.575 \\ DehazeFormer-T [51] & 35.05 & 0.989 & 3.21 & 30.36 & 0.973 & 4.13 & 0.686 & 12.90 \\ D4 [52] & 25.42 & 0.932 & 3.85 & 25.91 & 0.958 & 7.15 & 10.74 & 75.82 \\ ODCR [53] & 26.32 & 0.945 & 3.46 & 26.14 & 0.957 & 4.76 & 11.38 & 27.38 \\
**Ours** & **35.90** & **0.991** & **1.90** & **30.71** & **0.979** & **2.25** & 16.97 & 34.27 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Quantitative results of different methods on RESIDE-indoor and RESIDE-6K datasets. Best results are bolded, and the second-best results are underlined.  
Figure 10: Visual comparison of various methods on haze-free images. The first row shows the error maps between the resulting images and the ground truth.  
Figure 9: Visual comparison between our method and other approaches on the RESIDE-indoor dataset.  
Our method also demonstrates excellent performance in the outdoor image-dehazing task on the RESIDE-6K dataset. As shown in Figure 10, from the error maps, we can observe that traditional methods such as DCP exhibit higher error intensities, particularly in regions with heavy haze or complex textures, indicating their limitations in effectively removing haze and preserving color. The error distribution of GridDehazeNet remains relatively high, especially in challenging areas, suggesting that it still struggles with fine-grained haze removal and color consistency. The bright regions in their error maps highlight significant discrepancies from the ground truth. In contrast, other learning-based methods such as DehazeFormer-T, D4, and ODCR demonstrate reduced error levels, indicating improved performance in reconstructing clearer images. However, residual haze and color distortion remain noticeable in their results.  
Conversely, CCD-Net restores the details of the images, resulting in more natural colors that are closer to the ground-truth images (GT) on all datasets. Additionally, CCD-Net achieves the highest PSNR and SSIM values. The results show that CCD-Net recovered images with fog have better visual effects and the model is effective in image dehazing.