Surveillance Image Super Resolution Using SR - Generative Adversarial Network

Abstract

A single-image-super-resolution (SISR) is the process of converting a single low-quality (LR) image to a high-quality (HR) image. This technology is utilised in a variety of industries, including medical and satellite imaging, to retrieve quality and required information from blurred or overexposed photos. Because of the lack of ability to extract important data and images due to poor quality surveillance photographs, this method can be utilised in the field of surveillance to produce high-quality images. We'd like to use General Adversarial Networks to handle low-quality photos because existing methods have resulted in slightly fuzzy and greasy images that look like oil paintings (GAN). We'd like to introduce Super Resolution General Adversarial Networks in particular (SRGAN). This method employs perceptual losses. In this case, PSNR, MSE, and SSIM values are shown to be superior to those obtained by standard approaches in this case. The SRGAN-processed photos are of excellent quality, allowing the images to be seen through hazy and misty areas.