# A Single Image Enhancement Technique Using Dark Channel Prior
## 4 Experimental Results And Analysis
### Subjective Visual Evaluation  
In order to verify that the improved algorithm proposed in this paper has greatly improved the edge effect, an experimental simulation is performed. The obtained defogged restoration results are compared with defogged results of [PERSON]'s algorithm. Figure 6 illustrates the defogged images obtained by two defogging algorithms in different foggy scenes. Figure 5: Comparison results of different defogging algorithms, including cost function defogging algorithm [16], tolerance mechanism defogging algorithm [12], sky segmentation algorithm [15], and our proposed method. The first image and the second image in Figure 6b are defogged images obtained by using [PERSON]'s algorithm [11]. The overall color of images has a large chromatic aberration phenomenon. At the same time, an obvious edge effect appears in the tree outlines, the car peripheries, and on the signboards and the roads. This is mainly because the atmospheric light value in [PERSON]'s defogging algorithm is not appropriate. When there is a large white object in the image, the method incorrectly estimates the atmospheric light value. This will cause an inaccurate transmittance estimation and eventually cause an edge effect. The first image and the second image in Figure 6c are defogged images obtained by using our improved method. It can be seen that the defogged images we obtained not only make the color of the overall image very real, but also greatly improve the edge effects on the sides of trees, cars, signs, and roads. This is mainly because our improved method estimates the atmospheric light value by using a four-point weighting algorithm. This algorithm can deal with the situation that the estimation of the atmospheric light value is not accurate. In summary, the improved algorithm we proposed can subjectively improve the edge effect. Through the experimental simulation, it is verified that the improved method can achieve better defogged results on large areas with high brightness where the dark channel prior fails than [PERSON]'s method. Comparing our defogged images with current algorithms demonstrates that our method performs better on large areas with high brightness than other methods, such as defogging method based on cost function [16], defogging method based on tolerance mechanism [12], and defogging method based on sky segmentation [15]. Figure 7 illustrates the defogged images from four defogging algorithms. Furthermore, the two scenes in Figure 7 are a large sky area and a large reflective water surface. The first and second images in Figure 7b are defogged images obtained by using defogging algorithm based on the cost function [16]. It can be seen from the two images that the fog becomes darker, resulting in less detailed information in the image after defogging. This is mainly because the defogging method based on the cost function does not emphasize the refinement of transmittance. Figure 6: Comparison with [PERSON]’s algorithms [11] in different scenarios. The first picture and the second picture of Figure 7c are defogged images obtained by using defogging algorithm [12] based on the tolerance mechanism. It can be seen from the two pictures that the fog in the sky area of the first picture is still not removed. It shows large distortion on the water surface and the lake in the second image. This is mainly because the essence of the defogging algorithm [12] based on the tolerance mechanism uses a controllable parameter to distinguish the sky area from the non-sky area. The first image and the second image in Figure 7d are defogged images obtained by using segmentation algorithm [15]. It can be seen that most of the fog has been removed, but the thick fog has not been removed. This is mainly because the idea of this algorithm is to segment the sky and non-sky, and then perform different processing on the transmittance of the two parts. Among them, the transmittance of the sky region is set to a constant.