Enlighten GAN for Super-Resolution Images from Surveillance Car

Law enforcement and security officers utilize surveillance cars to monitor and investigate suspicious activities, including traffic violations and neighbourhood security. This paper delves into the comprehensive utilization of surveillance cars in modern society. By designing and developing an upgraded system, we aim to address ethical and legal concerns surrounding surveillance practices. The primary objective is to obtain super-resolution images from captured footage using Generative Adversarial Networks (GANs) for image enhancement. GANs, a type of Neural Network, enable the creation of high-quality images from existing low-resolution data. This study explores the application of GANs to enhance image quality in the context of surveillance cars. The proposed system leverages GANs to generate high-resolution images from the low-resolution ones captured by the surveillance car. Additionally, we provide a comprehensive review of the ongoing research and advancements in this field. Existing surveillance systems often output low-resolution images, but through the implementation of Enlighten GAN, we can achieve high-resolution results. The innovative integration of GAN technology empowers the surveillance car system to respond quickly to known situations with improved image clarity, enabling effective monitoring and investigation. This paper contributes to the advancement of surveillance capabilities by providing valuable insights into the potential of GANs for enhancing image quality and super-resolution in surveillance applications.