# Haze Removal Based on Refined Transmission Map for Aerial Image Matching
## 3 Results and Discussion  
The experiments were performed on four datasets to evaluate the performance of the proposed algorithm, namely, synthetic haze images, natural aerial images from Google, NH-HAZE [53,54], and manually annotated Datasets. NH-HAZE is a synthetic non-homogeneous haze dataset consisting of 55 outdoor images. The manually annotated dataset was created in the lab and consists of 18 images for testing the homogeneous aerial images. We present the results in Figures 2-9. The experimental results show that the proposed method performs well on all the datasets. Generally, haze-free images use a disparity map to generate synthetic haze images. The results of the different methods are shown in Figure 2. The results for the method of [PERSON] et al. show some block artifacts in certain regions. When observed using a uniform transmission map, the details of the transmission map cause image blur. However, the proposed algorithm reduced the block artifacts in the transmission map and enhanced the haze removal performance. Figure 3 shows the test images and the results obtained using the proposed method with aerial images. To evaluate the proposed method in Figures 3-5, natural aerial images were obtained from Google. The image sizes were 3264 \(\times\) 2448, 4318 \(\times\) 3083, 950 \(\times\) 691, and 3008 \(\times\) 2000. The performance of the proposed algorithm was also compared with those of several state-of-the-art dehazing methods when using aerial images, as shown in Figure 4. The proposed algorithm was used to process and analyze several large aerial images with a resolution of 3000 \(\times\) 3000 pixels. The Figure 4 are the input hazy images are compared with the proposed method and conventional methods such as [PERSON] et al. [35], and [PERSON] et al. [40,41]. The proposed un-optimized (MATLAB implementation) code could process each aerial image in approximately half an hour using a desktop computer (Intel 5, 3.1-GHz CPU, 8-GB RAM). The proposed method produced visually pleasing results that were superior to those produced by conventional methods. The results obtained using the proposed algorithm were generally sharper and clearer than those obtained using the method of [PERSON] et al. Although the image features could be easily detected when the image contrast was high, the method of [PERSON] and [PERSON] [35] performed poorly. Figure 3: Test aerial images and dehazing result with large sizes: (**a**) original hazy images and (**b**) proposed method. Figure 2: Images for each algorithm and their transmission maps: (**a**) hazy image, (**b**) [PERSON] et al. [40] method, and (**c**) proposed method. Figure 5 shows the detailed images based on magnifying the images in Figure 4. Figure 5c,d shows a block problem caused by block processing, whereas Figure 5b does not show the block problem. Block artifacts can occur in the case of mixed clouds and haze (e.g., very bright regions) when using block-based processing. However, the proposed algorithm reduces the number of block artifacts, as shown in Figure 5d. The reduction of block artifacts is not clear in the aerial images of the proposed method. Therefore, we compared their transmission maps, as shown in Figure 6. We can clearly see how the block artifacts have been smoothed compared to the DCP. For example, Figure 6a is a DCP transmission map containing block artifacts, and Figure 6b is a refined transmission map using the proposed method with reduced block artifacts. However, some of these may be affected by uncertainties; therefore, they require fuzzy preprocessing [55; 56; 57]. Figures 7  
Figure 4: Results of several algorithms for aerial images. Figure 5: Detailed images of Figure 4: (**a**) original hazy image, (**b**) [PERSON] and [PERSON] [35] method, (**c**) [PERSON] et al. [40] method, and (**d**) proposed method. best performance. The bold text indicates the best performance and the dark-green text indicates the next-best performance.