Infrared imaging has recently played an important role in a wide range of applications including surveillance, robotics and night vision. However, infrared cameras often suffer from some limitations, essentially about low-contrast and blurred details. These problems contribute to the loss of observation of target objects in infrared images, which could limit the feasibility of different infrared imaging applications. In this paper, we mainly focus on the problem of pedestrian detection on thermal images. Particularly, we emphasis the need for enhancing the visual quality of images beforehand performing the detection step. % to ensure effective results. To address that, we propose a novel thermal enhancement architecture based on Generative Adversarial Network, and composed of two modules contrast enhancement and denoising modules with a post-processing step for edge restoration in order to improve the overall quality. The effectiveness of the proposed architecture is assessed by means of visual quality metrics and better results are obtained compared to the original thermal images and to the obtained results by other existing enhancement methods. These results have been conduced on a subset of KAIST dataset. Using the same dataset, the impact of the proposed enhancement architecture has been demonstrated on the detection results by obtaining better performance with a significant margin using YOLOv3 detector.