GAN-Based Super Resolution Algorithm for High-Quality Image Enhancement

Abstract: Super Resolution: Leveraging investigates the trans-formative potential of Generative Adversarial Networks (GANs) in the context of super resolution, utilizing state-of-the-art algorithms to achieve high-quality image enhancement. The study encompasses a comprehensive analysis of GAN architectures suchas SRGAN, ESRGAN, and others, exploring their strengths, limitations, and practical applications. The proposed methodology delves into dataset selection, preprocessing techniques, and the integration of advanced algorithms within the GAN framework. It evaluates the quantitative and perceptual performance of these techniques, addressing challenges and proposing avenues for future research. The findings contribute to the evolving landscapeof GAN-based super resolution, offering insights for researchers and practitioners in advancing image enhancement technologies