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A Review on Image Super-Resolution Using GAN
Abstract
This study focuses on the utilization of generative adversarial networks (GANs) for generating high-resolution facial images from low-resolution inputs, which is vital for computer vision applications. Facial images present a complex structure, posing challenges for obtaining high-quality results using traditional super-resolution methods. However, recent advancements in deep learning, particularly GANs, have shown promising outcomes in this area. In this work, the authors conduct a comprehensive analysis of state-of-the-art GAN-based techniques for realistic high-resolution face image generation. They discuss the principles of image degradation, the learning process of GANs, and the challenges associated with these methods. By offering insights into the current state and future research directions, they aim to familiarize readers with the context and significance of GAN-based face image generation. This work highlights the importance of GANs in improving facial image quality and their relevance to advancing computer vision applications such as face verification and recognition.
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