# Nighttime Image Dehazing Based on Point Light Sources
###### Abstract
Affected by artificial light, such as street lights and car lights, the atmospheric light value at night is not uniform. Therefore, the daytime dehazing algorithms are not necessarily applicable for nighttime images. Furthermore, extensive previous experiments have demonstrated that some well-behaved image dehazing frameworks under daytime conditions cannot be directly applied to nighttime tasks. According to the analysis of scene conditions, nighttime images have the following characteristics: (1) it is difficult to determine the illumination intensity and range of artificial light sources. If there are many artificial light sources in the image, the pixel values of weaker light sources will also be affected by the strong light sources. Depending on the position of the imaging device, reflected light from objects in the captured image is attenuated differently. (2) In contrast to sunlight, the light emitted by artificial light sources is colored. The color description of an object in the nighttime image is the superposition of the color of itself and the color of the artificial light source. In view of the above-mentioned characteristics of atmospheric light in foggy scenes at night, some researchers take advantage of illumination compensation and color correction to modify spatially varying illumination. [PERSON] et al. [23] used the enhanced light intensity to balance illumination and corrected the color of incident light to estimate accurate ambient light; [PERSON] et al. [24] improved the dark channel prior to better adapt to nighttime dehazing tasks. In order to enhance the effect, they used color shift and local contrast correction. Though this method produces results with lower contrast and lower brightness overall; [PERSON] et al. [25] proposed a relatively smooth constrained layering algorithm to remove the glow effect; based on the Retinex theory [26], [PERSON] et al. [27] estimated the ambient illumination by channel differential guided filtering method, and they used the dark channel prior and the bright channel combination to evaluate the dehazing transmission map; paying attention to preserving the details of the fine structure area, [PERSON] et al. [28] decomposed the image into luminous image and non-luminous night image. They calculated the value of the atmospheric light and dark channels of each pixel in the non-luminous night image based on the superpixel method. Then, the transmission map is decomposed from fog-freeimage through the weighted guided image filter. All the above methods have achieved good results. However, past methods basically have some defects, such as color distortion, serious loss of texture details, and insufficient effect. Therefore, the task of dehazing nighttime images requires model reconstruction. Based on [PERSON]'s dark channel theory dehazing algorithm (DCP) [14], this paper proposes an improved nighttime dehazing algorithm for the influence matrix of artificial light sources. According to the light intensity of light source points, we filter the light source through the photometric map and obtain the corresponding light source influence matrix. The transmittance near the light source can be adaptively adjusted to reduce the glow effect near the light source by the light intensity matrix. The experimental results demonstrate that the method has great dehazing effect and detail retention at night, and the color cast problem of the restored image is also improved. Our main contributions are as follows:  
* The light source influence matrix is proposed to adaptively modify the transmittance near the artificial light source, so as to improve the dehazing task. * To enhance the detail of image, we used an edge filter to fuse the transmittance of the near and far light source areas. Furthermore, we used MRP to make the image more colorful. * For different fog concentration scenes, a bias coefficient is introduced to make the defogging algorithm suitable for dense fog scenes. The paper is organized as follows: Section 2 reviews the related work.