# Attention-Gate-Based Model with Inception-like Block for Single-Image Dehazing
## 1 Introduction
These mechanisms result in a greater weight in the blurred region, but the number of trainable parameters is too large. [PERSON] et al. [13] proposed a generic model-agnostic CNN that is composed of an encoder and decoder associated with residual blocks, and the whole network can be trained end-to-end which means that no physical knowledge should be obtained in advance. It is worth mentioning that this network architecture can be applied to other tasks in addition to image dehazing. The classic method based on unsupervised learning for the image dehazing problem involves the use of generative adversarial nets (GAN). The discriminator is used to determine authenticity, and the generator and the discriminator are trained separately through backpropagation. [PERSON] et al. [14] imported cyclic perceptual consistency loss into CycleGAN [15] to improve the quality of image dehazing, but it cannot recover color deviation and object edge well. [PERSON] et al. [16] proposed a two-scale generator and discriminator, and an enhancer with a multi-scale average pooling architecture has been constructed to provide more different receptive fields. In heavily hazy scenes, there is still room for improvement; therefore, it is necessary to apply more enhancing blocks to enhanced Pix2 pix [16]. [PERSON] et al. [17] modified CycleGAN [15] and conditional GAN [18] to formulate their image-dehazing model. The proposed model of [17] outperforms the aforementioned GANs, but it may generate unnatural colors for some scenes. However, GANs are difficult to train efficiently because the generator and the discriminator are hard to converge at the same time. In general, the model sizes of image-dehazing GANs are comparatively large, which has a significant impact on execution time. In practical applications, the image-dehazing algorithm belongs to the pre-processing part for autonomous driving systems; thus, while improving the quality of image dehazing, it is also necessary to consider the execution time and size of the trainable parameters. This paper proposes a lightweight CNN containing an attention gate, an inception-like block, and a spatial pyramid pooling (SPP) block with an appropriate loss function to achieve better image-dehazing quality. Finally, using an existing dataset, comparisons between other state-of-the-art and proposed image-dehazing algorithms based on image-dehazingquality, trainable parameters, and the average execution time per image are presented. In conclusion, the main contributions of this study can be summarized as follows:  
* It provides a real-time and lightweight end-to-end image-dehazing CNN model that restores the blur caused by the haze environment without intermediate component computation for ASM and knowledge of the physical model. * Before feeding hazy images to the CNN, an image pre-processing block was used to map normalized RGB images to different color spaces. The image pre-processing block increases the number of layers of feature maps such that the image-dehazing CNN can extract features effectively. * The inception-like block uses two \(3\times 3\) convolutions to replace one \(5\times 5\) convolution, which effectively increases the receptive field. The SPP block utilizes different pooling kernel sizes to extract feature values and to generate more feature maps. The proposed attention gate can smartly pay more attention to unclear structures, such as buildings and pedestrians. * Through an ablation study and a quantitative evaluation, this study demonstrates the advantages of using the image pre-processing block, the inception-like block, the spatial pooling blocks, and the proposed attention gate. * The number of trainable parameters of the image-dehazing CNN is 207.3 K, the model size is 0.86 MB, and the average execution time per frame under GPU acceleration is 0.018 s, which is equivalent to 55.56 fps.