# Nighttime Image Dehazing Based on Point Light Sources
###### Abstract  
Images routinely suffer from quality degradation in fog, mist, and other harsh weather conditions. Consequently, image dehazing is an essential and inevitable pre-processing step in computer vision tasks. Image quality enhancement for special scenes, especially nighttime image dehazing is extremely well studied for unmanned driving and nighttime surveillance, while the vast majority of dehazing algorithms in the past were only applicable to daytime conditions. After observing a large number of nighttime images, artificial light sources have replaced the position of the sun in daytime images and the impact of light sources on pixels varies with distance. This paper proposed a novel nighttime dehazing method using the light source influence matrix. The luminosity map can well express the photometric difference value of the picture light source. Then, the light source influence matrix is calculated to divide the image into near light source region and non-near light source region. Using the result of two regions, the two initial transmittances obtained by dark channel prior are fused by edge-preserving filtering. For the atmospheric light term, the initial atmospheric light value is corrected by the light source influence matrix. Finally, the final result is obtained by substituting the atmospheric light model. Theoretical analysis and comparative experiments verify the performance of the proposed image dehazing method. In terms of PSNR, SSIM, and UQI, this method improves 9.4%, 11.2%, and 3.3% over the existed night-time defogging method OSPF. In the future, we will explore the work from static picture dehazing to real-time video stream dehazing detection and will be used in detection on potential applications. n pacs: 12.10.Hm, 11.10.-p, 11.10.  
enhancement aims to emphasize and enhance some feature attributes of foggy images. [PERSON] [9] divides the image into several small blocks, and maximizes the contrast of each small block to remove fog. [PERSON] et al. (2012) [10] and [PERSON] (2013) [11] introduced fusion-based methods, which fused results, such as white balance, contrast enhancement, etc. [PERSON] et al. (2015) [12] proposed a non-reference perceptual fog density prediction model and enhanced images based on this model. Although image enhancement methods can improve the contrast of foggy images, there is still a lack of physical explanation for the results of dehazing technology. The physical model approach is mainly based on the atmospheric scattering model [13], which interprets foggy images as a combination of fog-free images and atmospheric lighting. Estimating transmission maps and atmospheric light maps are the two main tasks of physical model-based approaches. For the transmission map, many researchers have proposed their own solutions: dark channel prior estimation [14; 15], color attenuation prior estimation [16; 17], fog line prior method [18], etc. The learning-based method [19; 20; 21] is that researchers adjust parameters of neural network through training data to obtain the corresponding dehazing model. Many researchers also combine physical models and learning-based methods to accomplish this task. [PERSON] et al. [22] reformulated the atmospheric scattering model into a generative adversarial network, where the network model learns atmospheric light and transmittance from the data, and reworked the model to improve the interpretability of the GAN. [PERSON] et al. utilized the haze feature sequence obtained by self-encoding network to calculate the scene transmittance, finally substituted it into atmospheric scattering model to restore the dehazed image. However, deep learning methods require high-quality training data. In addition, some training methods are expensive, requiring the consumption of processors and GPUs. Almost all of dehazing strategies mentioned above are aimed at daytime. Generally, the sun is the main light source for daytime images, and the overall atmospheric light basically remains constant.