Enhancing Blurred Facial Images Using Generative Adversarial Networks

Abstract

This research examines the improvement of facial images using generative adversarial networks (GANs). The significance of this topic lies in its potential for enhancing image processing and facial recognition systems. The primary objective of this study is to evaluate the effectiveness of GANs in enhancing the quality of facial images. The hypotheses put forth in this thesis suggest that GAN-based methods can succeed in increasing the resolution and realism of facial images. The sample consists of 70.000 different facial images, representing the primary data source for this study. The method primarily involves the creation and training of a GAN model. A GAN consists of a generator that attempts to mimic real images during the learning process and a discriminator network that evaluates the realism of these images. The findings of the study demonstrate the effectiveness of GANs in making facial images higher in resolution and more realistic. This has the potential to improve the performance of facial recognition systems and enable more precise diagnoses in medical imaging applications. This information underscores the importance of GAN-based methods in enhancing facial images.