A comparative Analysis of Image Enhancement Using Autoencoders and Generative Adversarial Networks

Abstract

The purpose of this study, which focuses on image enhancement methods, is to investigate how various methods contribute to higher-quality photos in particular applications. Tracing the history of image improvement, encompassing the technological evolution of early photography from the 19th century to the present. The foundation of conventional image enhancement technology, which directly affects image elements and transformation coefficients, respectively, emphasizes frequency domain and spatial domain processing. It is noted that the application of autoencoders and generative adversarial networks to image enhancement as a deep learning training strategy has shown effectiveness in picture classification, target recognition, and image segmentation. This article primarily examines and contrasts autoencoders and generative adversarial networks, summarizing their applicability, constraints, benefits, and drawbacks and talking about the patterns in their upcoming development. The study has guiding value for advancing the development of picture technology and offers significant theoretical and empirical support for the field of image enhancement.