Detailed information from high-resolution images is needed when analyzing the content contained in the image. However, sometimes the image has a low resolution so the image is difficult to interpret. Based on these problems, research was carried out on improving the quality of image resolution, especially in the forensic field. CCTV video results that have low-quality images and are degraded with many noises, bad illumination, distortions, and blurs. So, we can remove that using Super-Resolution (SR) method. This can help during to identification, image interpretation, and analysis process clearly. Using Super-Resolution methods, a high-resolution image is obtained from a set of low-resolution images. The research was conducted based on the Super-Resolution Generative Adversarial Network (SRGAN). SRGAN is a generative model method that can generate data (images) with good quality. From the experiments that have been carried out, the system that has been built is proven to be able to produce images with good quality with the highest PSNR value is 24,727 and the highest SSIM value is 0.820.