Restoration and enhancement optimization of blurred images based on SRGAN

Abstract Blurred images pose a significant challenge in many applications, including medical imaging, remote sensing, and surveillance systems. These images suffer from low resolution, noise, and missing data, which can hinder their interpretation and analysis. Traditional methods for image restoration and enhancement have their limitations, such as low quality and slow processing times. To overcome these challenges, this paper proposes an innovative method using Super-resolution Generative Adversarial Networks (SRGANs) to enhance image quality and fidelity. The proposed method employs adversarial training, perceptual loss, residual learning, and feature reconstruction to generate visually realistic and high-quality super-resolution (SR) images from low-resolution (LR) inputs. The SRGANs approach outperforms traditional methods, demonstrating its potential to advance image restoration and enhancement techniques. The paper also discusses possible improvements and future directions for this technique.