## 3 Experimental Results and Analysis  
In order to verify the effectiveness of the proposed algorithm, the public dataset of O-HAZE with varied hazy densities is selected for comparison. In O-HAZE, the fog is a real haze generated by a professional haze machine. The same visual contents are recorded in both the hazy and dehazed conditions under the same light conditions. The dehazing methods of [PERSON] [26], [PERSON] [27], [PERSON] [15], and [PERSON] [19] are cited as comparisons. The results are shown in Figure 4. DCP is the conventional dark channel prior dehazing algorithm. FDCP is the method of [PERSON] [26]. GDCP is the method of [PERSON] [27]. MDCP is the method of [PERSON] [15]. FFA is the method of [PERSON] [19]. HDCP is the proposed method. Group (a) has abundant colors. The results are entirely distinct. The restored pictures of DCP and FDCP are too dark. The restored picture of GDCP has too much exposure, which leads to the loss of the original color. Meanwhile, the fog in the distant woods in the upper left of the picture is not completely removed. In order to show the line and color details of the restored image, enlarged contrasts in group (a) are added at the bottom of the figure, which focuses on enlarging the color cards placed in the figure. From the results, it can be seen that the processed pictures of DCP and FDCP are too dark, and the color contrast is low, which does not achieve the ideal situation. The results of GDCP are seriously blurred, and the details are markedly lost. The results of MDCP have been greatly improved, but the fog has not been completely removed. The FFA results are relatively complete overall, but the image is a bit dark. The quality of the image restored by the proposed method is significantly improved. The colors are richer and more realistic. The contrast and clarity are better. The texture details are clear. In group (b), the white of the chairs and the grey of the ground occupy a large part of the picture. The fog concentration of the original picture is relatively high, which makes the implementation of the dehazing algorithms more difficult. Additionally, the whole picture is blurred, and the textures are notably missing. The result of DCP directly suffers from severe color distortion. The result of FDCP has been improved, but the problems of dark picture brightness and insufficient texture details still exist. The results of GDCP still have the same problem. The brightness is too high, and the fog removal is incomplete. The result of MDCP is more realistic, but the problem of incomplete mist removal still exists. The result of FFA is generally grayish so that the entire photo appears unnatural. The results of the proposed algorithm are better. The fog removal is more complete. The contrast is higher. The picture is more realistic, where the contrast color card on the chair is more clearly visible. Group (c) mixes chairs, columns, and woods with more texture details behind them. The colors of the columns and chairs are not uniform, and the details of the woods are missing. The results of DCP are still dark and distorted. The overall appearance is bluish, especially for the pillars on both sides. The results of FDCP are similar to DCP. However, the  
Figure 4: Comparison of example pictures in the O-HAZE dataset. The processing results are, respectively, from: (I): DCP [20], (II): FDCP [26], (III): GDCP [27], (IV): MDCP [15], (V): FFA [19], and (VI): HDCP. The last row is the enlarged result of group a’s color card. color tone of the columns is more realistic and reasonable. GDCP still has high brightness and incomplete edge details. The resulting color of MDCP is biased towards situations similar to grayscale images, probably due to the modification of the prior theory of dark channels. The images of FFA have been improved significantly, but the contrast is slightly insufficient. The detailed information on the ground is incomplete. The result of the proposed method is more similar to reality, with significantly improved contrast and clarity.