## 4 Experiments
### Comparison with the State-of-the-Art  
**Quantitative evaluation. ** For dust-laden images, Table 1 displays the e-score and r-score values of various enhancement methods. It can be observed that both the traditional methods, MSRCR and Park, as well as the learning-based methods TBNN and USDR-Net, exhibit remarkable performance in terms of evaluation metrics. Park, as a traditional method, not only boasts a notable advantage in runtime but also secures top-three rankings in the evaluation scores. USDR-Net achieves second-best results through unsupervised adversarial learning, showcasing impressive generalization capability. However, in comparison, our method attains the best performance with significant improvements. This underscores the relatively gradual progress in the field of dust-laden image enhancement and demonstrates the accomplishments stemming from our exploration of correlations and uniqueness among similar images. Moving to the haze dataset as seen in Table 2, our approach showcases outstanding performance across various haze image datasets. It achieves the highest PSNR and SSIM indices in the challenging Dense-Haze dataset. TBNN not only demonstrates superior performance compared to other contrast methods but also maintains consistent performance across datasets. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Datasets**} & **DCP** & **MSRCR** & **RGHS** & **Park** & **TBNN** & **USDR-Net** & **Our** \\  & **e-Score/** & **e-Score/** & **e-Score/** & **e-Score/** & **e-Score/** & **e-Score/** & **e-Score/** & **e-Score/** \\  & **r-Score** & **r-Score** & **r-Score** & **r-Score** & **r-Score** & **r-Score** & **r-Score** \\ \hline D-Sand & 0.1214/1.2073 & 0.7192/1.8406 & 0.4307/1.3385 & 0.6832/1.5354 & 0.5204/1.6448 & 0.8643/1.8511 & 1.2308/2.2216 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Quantitative comparison of real-word sand dust images. **Qualitative evaluation. ** Figure 4 showcases the training results for real dust-laden images. Among traditional methods, only the MSRCR and Park algorithms exhibit effective dust removal, while other traditional methods fail to eliminate dust and introduce color distortions. Learning-based methods show varying degrees of enhancement, with our method demonstrating the best visual effect by achieving comprehensive dust removal and color restoration. Moving to Figure 5, we present enhancement results for synthetic and real haze images. Most methods display good defogging effects on synthetic haze images, but residual haze remains evident in enhancing real haze images. TBNN attains remarkable visual quality. In comparison, our method removes more haze and restores clearer image details. Figure 6 vividly displays the enhancement outcomes for underwater images. In contrast to other methods, our approach successfully mitigates underwater light refraction-induced occlusion and color bias issues, effectively restoring image details. By delving into the connections and characteristics among the three image categories, we employ a multi-task knowledge processing mechanism to avoid interference between different tasks. This allows us to fully exploit these connections, enhancing the performance of individual tasks and ultimately achieving outstanding results.