Yildiz,B.Software Engineering2024-07-052024-07-0520221978-166547010-010.1109/UBMK55850.2022.99195202-s2.0-85141825651https://doi.org/10.1109/UBMK55850.2022.9919520https://hdl.handle.net/20.500.14411/4064Super-resolution is the process of generating high-resolution images from low-resolution images. There are a variety of practical applications used in real-world problems such as high-definition content creation, surveillance imaging, gaming, and medical imaging. Super-resolution has been the subject of many researches over the past few decades, as improving image resolution offers many advantages. Going beyond the previously presented methods, Generative Adversarial Networks offers a very promising solution. In this work, we will use the Generative Adversarial Networks-based approach to obtain 4x resolution images that are perceptually better than previous solutions. Our extensive experiments, including perceptual comparison, Peak Signal-to-Noise Ratio, and classification success metrics, show that our approach is quite promising for image super-resolution. © 2022 IEEE.eninfo:eu-repo/semantics/closedAccessDeep LearningGenerative Adversarial NetworksImage ProcessingMachine LearningSuper ResolutionConference Object104109