# A Single Image Enhancement Technique Using Dark Channel Prior
## 5 Conclusions  
Based on the traditional dark channel prior model, this paper proposes several improvements for three shortcomings (i.e., high time complexity, edge effect, and failure of dark channel prior) of [PERSON]'s algorithm. First, a four-point weighting algorithm is used to accurately estimate the atmospheric light value, and guided filtering is introduced instead of the more complex soft matting algorithm to refine the coarse transmission map. In this case, not only the processing speed of the algorithm is improved, but also the edge effect is effectively improved. Then, the sky area does not conform to the dark channel prior principle, which can cause halo distortion in sky areas. An algorithm combining edge detection and maximum inter-class variance is proposed to separate the sky area from the non-sky area, and then an enhanced algorithm via sequential decomposition is applied to the sky area. Experimental results show that the improved methods not only improve the visual effect of the defogged image subjectively, but also objectively evaluate that the defogging effect of our proposed method is more thorough and the recovered details are more abundant than other methods.  
Methodology, C.W., Y.Z. and L.W.; Supervision, M.D. All authors have read and agreed to the published version of the manuscript.  
This work is supported by the China Postdoctoral Science Foundation (Grant No. 259822), the National Postdoctoral program for Innovative Talents (Grant No. BX20200108), the National Science Foundation of China (Grant No. 61976070), and the Science Foundation of Heilongjiang Province (YQ2020F005).  
**Institutional Review Board Statement**: Not applicable.  
**Informed Consent Statement**: Not applicable.  
**Data Availability Statement**: Data is contained within the article.  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline \multirow{2}{*}{**Methods**} & \multicolumn{2}{c}{**Scenario 1**} & \multicolumn{2}{c}{**Scenario 2**} \\ \cline{2-5}  & **PSNR** & **SSIM** & **PSNR** & **SSIM** \\ \hline Cost function defogging method [16] & 13.25 & 0.75 & 8.79 & 0.46 \\ \hline Tolerance mechanism defogging method [12] & 12.98 & 0.77 & 9.15 & 0.46 \\ \hline Sky segmentation defogging method [15] & 15.12 & 0.90 & 11.37 & 0.63 \\ \hline Ours & 18.70 & 0.92 & 13.32 & 0.74 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Quantitative results of other defogging methods and our proposed method in the images of Figure 7.  
\begin{table}
\begin{tabular}{l c c} \hline \hline \multicolumn{1}{c}{**Method**} & **PSNR** & **SSIM** \\ \hline Cost function defogging method [16] & 11.58 & 0.66 \\ \hline Tolerance mechanism defogging method [12] & 13.16 & 0.72 \\ \hline Sky segmentation defogging method [15] & 15.06 & 0.88 \\ \hline Ours & 19.20 & 0.91 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Quantitative results of other defogging methods and our proposed method in the test set.  
**Conflicts of Interest:** We would like to note that in the manuscript entitled \"A Single Image Enhancement technique using Dark Channel Prior\", no conflict of interest exits in the submission of this manuscript, and manuscript is approved by all authors for publication.