# Saturation-Based Airlight Color Restoration of Hazy Images
## 4 Experimental Results and Discussions
### Performance Evaluation Using Ground Truth Image  
This study used PSNR, CIEDE2000 [39], and CIE94 [40] as evaluation indexes for the assessment of dehazing performance and color restoration performance. PSNR evaluates the quality of images, and CIEDE2000 and CIE94 calculate color differences between the original and restored images to evaluate the tone of the images. For comparison, we presented the scores of CIEDE2000 and CIE94 from the average and the square root of the average on the CIEDE2000 and CIE94 maps, respectively. In this context, higher PSNR scores indicate better performance, while lower CIEDE2000 and CIE94 scores indicate superior performance. Unfortunately, no actual haze dataset with biased colors for an objective assessment index has been proposed for open source. Thus, we used Dense-HAZE [41], a popular dataset that offers actual hazy images, as our input images. The Dense-HAZE dataset provides real hazy and ground truth images using specialized fog-generating machines on haze-free scenes as input images. In particular, Dense-HAZE generated a bluish haze in all images, even though there was no added color because the fog machine produced dark-colored fog. Thus, we used Dense-HAZE to evaluate both the visibility and color restoration performance in color-biased hazy images. The Dense-HAZE images used and the results from each algorithm are shown in Figure 12, and the quantitative evaluation results from Figure 12 are presented in Table 2.  
Dense-HAZE images in Figure 12 had limitations for visual comparison due to the dense haze, compared with the images shown in Figure 11. However, the results from HRDCP and NGCCLAHE showed that the images resulted in considerable performance in the edges due to high dehazing performance, and due to strong contrast and white balance, the overall images showed gray tone. In addition, WSDACC added a strong red color, opposite to the blue haze, resulting in a new color cast in the output images. In contrast, our method increased the visibility of the hazy areas and preserved the object color, similar to that of the ground truth image. Our results, in particular, accurately recognized the original object colors and, similar to NGCCLAHE's results, naturally removed haze in some areas. The quantitative comparison of the restored images from Dense-HAZE in Table 2 showed the best results for the proposed method, while in the existing methods, NGCCLAHE showed superior results both in PSNR and CIEDE2000, as well as CIE94, and HRDCP showed high performance almost similar to the method proposed in PSNR in some areas. However, the proposed method showed a particularly considerable deviation in CIEDE2000 and CIE94 compared with the existing methods, including HRDCP, demonstrating color restoration performance closer to the original. Because the very thick haze in each image of Figure 12 made visual confirmation difficult, the O-HAZE [42] dataset was used for visual and quantitative evaluations of the ground truth images. While O-HAZE offered actual hazy images in the same way as Dense-HAZE, the spreading of haze in normal concentration allowed for a visual comparison and the determination of the effect of the color restoration algorithm on general hazy images. The results of the visual comparison of O-HAZE in this study are shown in Figure 13, and the quantitative results are shown in Table 3.  
This paper compares the processing time of HRDCP, NGCCLAHE, and WSDACC with the proposed method, using O-HAZE and Dense-HAZE datasets. The input images for the experiments are the fog images in Figures 12 and 13, and the respective processing times are shown in Table 4.  
Figure 12: Comparison of the resulting images of the Dense-HAZE dataset: (**a**) ground truth image; (**b**) hazy image; (**c**) HRDCP; (**d**) NGCCLAHE; (**e**) WSDACC; (**f**) proposed dehazing method.