# Enhancing Image Dehazing with a Multi-DCP Approach with Adaptive Airlight and Gamma Correction
## 5 Evaluations
### Ablation Study  
In this section, we conduct an ablation study to assess the impact of various components introduced in our method on dehazing performance. Table 2 shows that the proposed enhanced transmission map generated from inverted and Euclidean difference images contributes most significantly, outperforming [PERSON]'s approach [4] by 7.59 points despite the absence of guided filtering, adaptive AR estimation, multiscale processing, or adaptive gamma correction.  
In Figure 7c, residual haze can be observed surrounding objects, suggesting additional refinement is necessary. We employed a \(1\times 1\) DCP alongside a guided filter to tackle this issue. While this approach led to a marginal decrease of approximately three points  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**RTTS** [1S]} & **[PERSON]** & **[PERSON] et al.** & **[PERSON] et al.** & **[PERSON]** & **[PERSON] et al.** & **[PERSON] et al.** & **[PERSON] et al.** & **[PERSON] et al.** & **[PERSON]** & **[PERSON]** \\  & [2] & [3] & [4] & [5] & [6] & [7] & [8] & [13] & [11] & [12] & **Ours** \\ \hline Average DHQI \(\uparrow\) & 46.62 & 53.86 & 47.91 & 41.79 & 46.37 & 50.03 & 53.31 & 45.34 & 44.37 & 47.37 & **56.89** \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparisons with various competing image dehazing methods.  
Figure 6: (**a**) Original haze image. (**b**) Transmission map based on [PERSON] et al. [4]. (**c**) Transmission map based on our proposed method. (**d**) Dehazed image based on [PERSON] et al. [4]. (**e**) Dehazed image based on our proposed method.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Transmission Map**} & \multirow{2}{*}{**Patch Size**} & \multirow{2}{*}{**Airlight Estimation**} & \multirow{2}{*}{**Multiscale**} & \multicolumn{2}{c}{**Adaptive Gamma**} & \multirow{2}{*}{**DHQI**} \\  & & & Original & X & X & **47.91** \\ \(r^{\mu}(x)\) & & Original & X & X & **58.07** \\ \(r^{\mu}(x)\) & & Improved & X & X & 55.82 \\ \(r^{\mu}(x)\) & & Original & X & O & 55.50 \\ \(r^{\mu}(x)\) & & Improved & X & O & 55.92 \\ \hline \hline \(r^{\mu}(x)\) & & Original & X & X & 48.94 \\ \(r^{\mu}(x)\) & \(1\times 1\)* & Improved & X & & 52.74 \\ \(r^{\mu}(x)\) & & Original & X & O & 56.40 \\ \(r^{\mu}(x)\) & & Improved & X & O & 56.68 \\ \(r^{\mu}(x)\) & & Improved & O & O & **56.89** \\ \hline \hline \end{tabular}  
*—without guided filter, **—**with guided filter.  
\end{table}
Table 2: Ablation study of the impacts of different components in the proposed method.  
in the DHQI, as shown in Table 2, the integration of adaptive gamma correction yielded an approximate one point improvement. Moreover, incorporating multiscale processing techniques and adaptive AR estimation contributed to further performance enhancement, as illustrated in Figure 7d.  
Figure 8 visually demonstrates the efficacy of employing a \(1\times 1\) DCP and guided filter combination for more precise and detailed dehazing, consequently improving object detection performance. The contrast between using [PERSON] et al. [4] and our \(1\times 1\) DCP and guided filter is highlighted in Figure 8b and 8c, respectively. Additionally, as depicted in Figure 8d, a multiscale approach boosts object detection confidence.  
Ultimately, integrating all components, our method achieves a notable numerical improvement of approximately nine points over the approach by [PERSON] et al. [4].