# A Nighttime and Daytime Single-Image Dehazing Method
## 2 Background Theory and Related Work
### Related Works  
As previously discussed, most dehazing methods are applied to daytime scenes. The recent methods are basically based on the atmospheric scattering model and remove haze using various prior methods; for example, [PERSON] et al. 's [9; 10] dark channel prior, [PERSON] et al. 's [11] color-line prior, [PERSON] et al. 's [12] color attenuation prior and [PERSON] et al. 's [13] haze-line prior. Much of the related work is carried out on the basis of the dark channel a priori. [PERSON] et al. [14] proposed a contextual regularization dehazing method by exploring the inherent boundary constraint on the transmission function. [PERSON] et al. [13] proposed a dehazing method using haze lines. However, the result exceeds the lower bound of the constraint. Subsequently, [PERSON] et al. [15] improved the optimization method of their model. Although in unevenly illuminated areas, such as the sky and lakes, the transmission estimation is inaccurate, resulting in excessive correction or haze residue. In general, the prior methods are liable to failure in different scenes. In recent years, with the development of Convolutional Neural Networks (CNNs), deep learning has also been applied to dehazing research. [PERSON] et al. [16] did not separately estimate the transmission and ambient illumination but directly generated clear images through a lightweight CNN. [PERSON] et al. 's [17] dehazing network focuses on solving the problem of artifacts. [PERSON] et al. [18] embedded the dehazing feature unit into the network and reconstructed the clear image after extracting the features through the encoder module. [PERSON] et al. [19] proposed an end-to-end domain adaptive network to dehaze the real image. [PERSON] et al. 's [20] method is a multi-scale enhanced dehazing network that integrates different levels of features based on back-projection technology. Another type of method is based on Generative Adversarial Networks (GANs). [PERSON] et al. [21] embedded the adversarial network in the enhanced Pix2 pix dehazing network and optimized the training model through four loss functions. [PERSON] et al. [22] proposed adding a normalization layer to the dehazing network model to obtain image information. However, the above-mentioned daytime dehazing methods are not applicable at night. This is because daytime ambient illumination is a fixed value, which is determined by the attenuation and scattering of atmospheric light, while nighttime ambient illumination is a local variable. Daytime dehazing methods usually estimate the ambient illumination with the brightest area of the image. However, in the nighttime scene, the ambient illumination is mainly affected by artificial light sources, such as street lights and car lights. Moreover, the complex environmental conditions make the deep learning dehazing model unable to process the nighttime haze image. Therefore, it is necessary to deal with the nighttime haze image in a targeted manner. Aiming at the characteristics of low contrast, low brightness, and light discoloration of nighttime haze images, [PERSON] et al. [23] proposed using color transfer for preprocessing and removing haze using a dark channel prior and a bilateral filter; however, this method introduces distortion. [PERSON] et al. [24] performed illumination compensation and color correction for the nighttime haze image and used the dark channel prior to remove the haze; however, this caused artifacts to appear. Then, [PERSON] et al. [25] proposed a maximum reflectance prior to estimate the ambient illumination, but it changes the original color of the image. [PERSON] et al. [26] improved the traditional atmospheric scattering model and introduced a glow layer, and dehazed the new separated haze image and reduced the glow using a dark channel. Although the glow problem can be effectivelysolved, the color of the output image is unreal. [PERSON] et al. [27] optimized their original model and estimated ambient illumination through different window sizes, using a multi-scale fusion method to dehaze.