# Image Dehazing Algorithm Based on Deep Learning Coupled Local and Global Features
## 4 Experimental Results and Analysis
### Experimental Results of Non-Uniform Haze Images  
Since most existing deep learning-based defogging algorithms use a uniform dataset to train their own defogging models, the excessive pursuit of higher evaluation metrics on a single dataset leads to poor generalization ability of the models. Although most of the defogging algorithms can obtain good results on the SOTS dataset, they are not fully effective in recovering clear and fog-free images in the face of a complex and variable real haze environment. Therefore, in order to improve the generalization ability of this paper's defogging model and better improve the effect of recovering real haze pictures, based on the above problems, this paper proposed and produced a non-uniform haze dataset. The NTIRE2020-Dhaze dataset provided by the NTIRE2020 Image Recovery Challenge is shown in Figure 5b. This dataset is different from the synthetic dataset in that it was produced by a professional haze machine with real haze to generate real hazy images, and although the real dataset seems more attractive, it is difficult to obtain enough images. In this paper, the non-uniform haze dataset was based on the first 50 high-resolution image samples in the NTIRE2020-Dhaze dataset, and 4800 samples were synthesized from the original samples by random cropping, flipping and rotating for training, and the last five images of the dataset were used for testing. In the experiments of the non-uniform haze images, the experimental results of five images tested after using the NTIRE2020-Dhaze dataset are shown in Figure 6. It can be seen from Figure 6 that the method used in [5] was less effective in removing haze as it \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Dataset** & **Evaluation** & **Ref.** & **Ref.** & **Ref.** & **Ref.** & **Ref.** & **Propose** \\
**Indicators** & **[5]** & **[6]** & **[11]** & **[12]** & **[15]** & **Method** \\ \hline \multirow{2}{*}{SOTS} & PSNR & 15.42 & 19.52 & 21.58 & 25.22 & 30.28 & 27.94 \\  & SSIM & 0.74 & 0.83 & 0.8 & 0.86 & 0.92 & 0.88 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The quantitative evaluation of different defogging methods on the SOTS dataset. Figure 5: The training dataset: (**a**) Outdoor dataset; (**b**) NTIRE2020-Dhaze dataset. contained a large area of white bright area after fogging, which appeared as too much exposure (such as in Figure 6b of the first, second, fifth) and in other areas after fogging, the overall color was dark. The defogged image of the method used in [6] solves the problem of overexposure after defogging the dark channel theory for bright areas containing large white areas, but the color deviation between the defogged image and the standard fog-free image was large, and the overall image was seriously dark (such as in Figure 6c). The method used in [11] for the test images was found to remove only the thin haze at the edges of the images, however, it was not effective for the areas with a higher haze concentration (such as Figure 6d). The method of fog removal in [12] was unable to remove the areas with high haze concentration, while the fogged images showed local color deviations, (such as the yellowish color of the lawn in the first, second, and third panels of Figure 6e differed significantly from the standard fog-free images). When the results of the dehazing method in [15] and the dehazing algorithm in this paper were compared with the real fog free images, it could clearly be seen that the algorithm in this paper effectively removed the mist and fog in the images. There was no color distortion, and it was closer to the real fog-free image than that of other methods. The quantitative evaluation of different defogging methods on the NTIRE2020-Dhaze dataset is presented in Table 2, from which it is clear that the algorithm in this paper was higher than the comparison methods in terms of the PSNR and SSIM obtained on the non-uniform haze datasets.