A New Approach to Improving Photorealistic Image Quality Using Generative Adversarial Networks (GANs)

Abstract

The current study explores the use of generative adversarial networks (GANs) to improve image quality in multiple applications. The research was designed to investigate the effectiveness of the proposed model in improving image quality through experimental and analytical steps. Data processing includes collecting data from diverse sources, cleaning it, converting it to a standardized format, and then dividing it into training and test sets. An advanced algorithm using GANs was applied to improve image quality, utilizing advanced deep learning techniques. The model was evaluated using metrics such as the Fréchet Intradistance (FID) to measure the quality of the generated images. The results demonstrated that the model is capable of generating high-quality images while preserving fine details. The study also demonstrated that the model has the ability to generalize across multiple experiments. The benefits of this technique were noted in fields such as healthcare, education, and digital art. The study recommended improving training stability and image quality, and expanding the use of this technique to new fields such as artificial intelligence.