# Detection of Targets in Road Scene Images Enhanced Using Conditional GAN-Based Dehazing Model
## 2 Related Work  
Many deterministic image dehazing methods have been proposed, with the assumption of prior information or a physical model. [PERSON] and [PERSON] [7] reviewed the research on image dehazing. The methods, in accordance with the processing technique used, can be categorized as image enhancement, multi-image fusion or image restoration. Image enhancement methods aim to improve the visibility with image processing algorithms. Multiple input images can be fused to generate the dehazed image. These two approaches do not rely on a physical model of hazy image formation. On the contrary, restoration-based methods adopt a degradation model. Image visibility is improved by reversing the degradation processes. [PERSON] et al. [8] proposed a method based on the dark channel prior (DCP) for single image dehazing. They observed the presence of low intensity pixels within local regions in haze-free images. Formulations were then devised for the computation of transmission and airlight. With these parameters and the assumption of the physical model, the scene radiance was restored. As presented in Section 5, DCP has a problem of color distortion. [PERSON] et al. [9] proposed a method to correct the color and enhance the contrast of hazy images. [PERSON] [10] utilized multiple-exposure images and a Laplacian blending scheme for image dehazing. Pixelwise hazy-free color is also estimated from the physical model formulation. [PERSON] et al. [11] implemented a multi-exposure framework for haze removal of images represented in the hue saturation value (HSV) color space. To avoid color distortion, the hue channel is not processed. The multiple-exposure images are generated with the gamma factor varied incrementally. [PERSON] et al. [12] proposed a framework which is comprised of hybrid median filtering for visibility restoration, Laplacian filtering for initial dehazing, and just noticeable difference-based boosting for image detail enhancement. These studies exploit hand-crafted features in image dehazing, while many recently proposed models adopt deep learning approach. In some applications, deterministic algorithms can achieve competitive, or even better performance, than deep learning models. For instance, [PERSON] et al. [13] demonstrated that handcrafted features perform better than CNN-based descriptors in texture analysis. Recently, more image dehazing research adopted a data-driven approach. The image dehazing model was developed via deep learning from training data. [PERSON] et al. [14] proposed a hybrid method called AOD-Net, in which a CNN is trained to generate a transmission map from the image samples. A haze-free image is then computed with the input of a transmission map and the formulation of an atmospheric scattering model. However, the images generated by AOD-Net may be dark. [PERSON] and [PERSON] [15] proposed a two-stream network to predict the transmission map and airlight. The correlation of the generated dehazed image and the estimated transmission map are analyzed with a generative adversarial network (GAN). [PERSON] et al. [16] also proposed a GAN framework for single image dehazing. The generator network has an encoder-decoder structure. The frequency information, computed from the ground-truthed image and generator output image, is then the input for the discriminator network. Although deep learning models can be trained to produce very good results with benchmark datasets, their performance can deteriorate significantly on unseen images. In summary, these methods either combine a deep learning network with the formulation of the atmospheric scattering model, or embed a deterministic computation module to a CNN. Instead, we propose a novel single network that can be trained end-to-end to generate a haze-free image without pre-processing or additional modules. [PERSON] et al. [17] proposed an image dehazing model combining transformer and CNN modules. The transformer, with the use of prior 3D information, aims for global modeling. The CNN encoder is capable of local modeling.