# A Nighttime and Daytime Single-Image Dehazing Method
## 1 Introduction  
The suspended particles in haze weaken the spread of light, resulting in the loss of image information and the low contrast and saturation of the collected images, which has a negative effect on visual systems, such as those used for video surveillance and autonomous driving. Therefore, many scholars have researched this aspect, especially when applied to monitoring [1] and image segmentation [2].  
Dehazing technology has achieved good results in daytime image processing. There are some methods that are used to directly enhance images, for instance, methods based on histogram equalization [3] or Retinex theory [4; 5; 6; 7]. These types of methods enhance the characteristic areas of the image and weaken the areas of no interest according to human perception. However, these methods do not consider the cause of haze in the image, nor do they remove haze in terms of contrast and color. The current methods are basically based on the atmospheric scattering model [8] and use various prior methods that have generated unsatisfactory results for nighttime scenes.  
Nighttime scenes are more complicated than daytime scenes. This is mainly because artificial light sources at night make the ambient light spatial. As a result, there are few pieces of research that have investigated dehazing at night, and only a few methods are effective for these images; furthermore, these methods change the color of the original image.  
In this paper, we first propose an effective single-image dehazing method called MF Dehazer that can be effectively applied to both nighttime and daytime scenes. The quality of the haze-free images depends on the accuracy of the ambient illumination estimation and the transmission estimation. Secondly, we analyzed the ambient light characteristics of the nighttime images and combined the atmospheric scattering model with the Retinextheory. We also propose a mixed-filter method in order to obtain ambient illumination. Additionally, in order to estimate transmission, the boundary-constrained method was used to estimate the initial transmission and then performed high-light area transmission compensation for the light source area. Finally, we optimized the transmission through regularization. The experimental results demonstrate that regardless of whether it is a nighttime or daytime scene, the image quality obtained using this method is superior when compared to the existing algorithms.  
The contribution of our paper can be summarized as follows:  
1. Due to the influence of artificial light sources in nighttime haze images, the light is directional and spatial. The mixed-filter method we designed can accurately estimate the direction, shape and color of the image's light.
2. In terms of transmission estimation, we analyzed the reasons for the light source diffusion problem seen in other nighttime dehazing methods. This is because the transmission of the light source area is underestimated. Therefore, we proposed a method to compensate for the high-light area transmission in order to weaken the dehazing effect of the light source areas to solve this problem. Consequently, regularization can improve the contrast of the image and solve the problem of excessive dehazing or poor dehazing effects.
3. Compared to other methods, MF Dehazer can be used effectively for different images, such as images of both distant and close views and nighttime or daytime images. In addition, haze-free images retain their original color and enhance the dark areas, providing a novel approach for removing haze.