# An Enhanced pix2 pix Dehazing Network with Guided Filter Layer
## 1 Introduction  
Haze has become a traditional climate phenomenon [1], and is one of the factors that early warning weather forecasting takes into account. This phenomenon makes it difficult to acquire data by means of computer vision equipment, as haze results in color distortion, blurring and reduction in the contrast of image data acquired by this equipment. Thus, haze affects the wider application of a visual system [2]. Many algorithms exist in the field of image dehazing. The most successful methods are based on an atmospheric scattering model [3], which can be expressed as:  
\[I(x)=J(x)+(A-J(x))(1-t(x)) \tag{1}\]  
where \(x\) represents the pixel location, \(I(x)\) represents the observed hazy image, \(J(x)\) is the clear image, A is the global atmospheric light called the airlight [3,4] and \(t(x)\) represents the transmission map. According to this model, obtaining a clear image relies on estimation of the atmospheric light and the transmission map. Many previously attempted methods are based on prior information, such as dark channel prior (DCP) [4], which uses dark channel features to estimate transmission maps. These methods, based on prior information, achieve good results, but the acquisition of prior information in practical applications is affected by certain factors that present variable characteristics, making it impossible to accurately estimate the transmission map and resulting in unclear current predictions of haze-free images, as shown in Figure 1. Since 2006, many methods of image dehazing using convolutional neural networks have been employed. Many of these are based on the atmospheric scattering model. Therefore, the quality and effectiveness of these methods depend to a large extent on the estimation of the atmospheric light value and the transmission map. To eliminate the influence of the inherent physical model on the dehazing image, some methods use a deep neural network, such as Deep Residual Learning (DRL) [5], to directly learn mapping from a hazy image to produce a haze-free image. DRL [5] uses Residual Network (ResNet), which consists of 13 layers, to learn mapping. With the successful application of an image-style transfer based on a generative adversarial network (GAN) [6], we can liken the process from a hazy to haze-free image to the transition between two styles of images. However, the image-style transfer method cannot be directly applied to image dehazing because the haze concentration at different pixels of the image and at different scene depths is not uniformly distributed. Also, a halo-like [7] effect and detail loss cannot be avoided in most dehazing methods. Considering that many image preprocessing techniques are effectively applied to image dehazing, such as digital filtering methods, time-scale or time-frequency multidimensional signal decomposition and reconstruction methods, a modified dehazing model using [PERSON]'s adaptive filter was proposed in [8], and a multiplicative noise removal method based on sparse analysis model with enhanced regularization was proposed in [9]. Thus, we here use a method to introduce a guided filter into the network and use a guided filter layer to construct a residual channel filter to retain the edge and details of the hazy image to a maximum extent. In this way, the filter effectively helps the generator suppress the halo-like effect produced by the dehazing process. In this paper, we propose a pix2 pix dehazing network combined with a residual guided filter layer. This network includes three parts, the generator, the discriminator and the residual channel guided filter. We enhanced the network by adding a measure of perceptual loss and reducing the size of the pix2 pix network to be suitable for dehazing. To reduce the halo-like effect, we design a filter to obtain the contour information of the hazy image, which is then combined with the enhanced pix2 pix network. Figure 1: Image dehazing examples with and without prior information. (**a**) Input hazy image; (**b**) dark channel prior (DCP); (**c**) ours; (**d**) ground truth.