# Attention-Gate-Based Model with Inception-like Block for Single-Image Dehazing
## 1 Introduction  
Low-visibility environments have a significant influence on vision-based autonomous driving systems. For example, haze can cause serious damage to image quality, including color and brightness deviations that lead to numerous visual information losses. Consequently, the performance of computer vision algorithms, such as object detection, semantic segmentation, and visual simultaneous localization and mapping (visual SLAM), are degraded, and the safety of autonomous car driving that relies heavily on the above-mentioned algorithms is jeopardized accordingly. To preserve the performance quality of these vision-based algorithms, the images obtained by cameras require preprocessing before feeding to these functional processes. The main cause of haze is the scattering of atmospheric particles. [PERSON] et al. [1] divided image-dehazing methods into two categories depending on whether an atmospheric scattering model (ASM) [2] is used. [PERSON] [3] mentioned that single-image dehazing using an ASM may face an ill-posed problem due to the availability of the transmission map and atmospheric light. Therefore, statistical experience is required to obtain a reasonable assumption called the prior, such that the ASM-based approach can be applied to a single-image dehazing problem. [PERSON] et al. [4] obtained the dark channel prior (DCP) from large numbers of outdoor images by statistical methods. This is because the dark channel value of the haze-free outdoor image captured during daytime is close to zero, except for the sky and white areas. Other priors were also proposed in the literature. [PERSON] et al. [5] found that the difference between brightness and saturation is proportional to the depth,which is the so-called color attenuation prior (CAP). The haze-line prior [6] is based on the observation that the pixel values of a hazy image can be modeled as lines in the RGB space with atmospheric light as the origin. The distance from the origin of the same haze line is affected by the transmission map. [PERSON] et al. [7] added an absorption function to the ASM and solved the image dehazing problem with the gray-world assumption. Based on DCP, [PERSON] et al. [8] improved the estimation method of atmospheric light to avoid mistaking the sky region as containing haze and smoothed the transmission map with two morphological operators, namely, dilation and erosion. The recent development of graphics processing units (GPUs), which deliver extraordinary acceleration in workloads involving artificial intelligence and machine learning, leads to an alternative dehazing approach based on these related technologies and becomes a research hotspot without using the ASM. Deep learning methods can be divided into supervised and unsupervised learning methods. The method based on supervised learning requires both hazy and ground truth images as inputs to a specially designed convolutional neural network (CNN) and trains a dehazing network using an appropriate loss function and backpropagation. Since the transmission map and atmospheric light are required when using the ASM approach, [PERSON] et al. [9] set the atmospheric light to 1 and constructed a network structure called DehazeNet to estimate the transmission map. However, because the atmospheric light is defined in advance, the result of image dehazing is poor. [PERSON] et al. [10] integrated the transmission map and atmospheric light into a function as the learning target of the proposed AOD-Net. Furthermore, [PERSON] et al. [8] redesigned the network architecture of AOD-Net [10], inheriting the advantages of few trainable parameters and real-time capabilities. Based on the idea of integrating the transmission map and atmospheric light as the learning target, [PERSON] et al. [11] designed a multi-scale network architecture using \(1\times 1\) convolution and pooling. [PERSON] et al. [12] concatenated two attention mechanisms to FFA-Net: channel attention and pixel attention.