Image resolution enhancement using GAN

Image resolution enhancement using Generative Adversarial Networks (GANs) has emerged as a powerful technique in the field of computer vision. GANs consist of two neural networks—the generator and the discriminator—that work in tandem to produce high-quality images. The generator aims to create realistic high-resolution images from low-resolution inputs, while the discriminator evaluates the authenticity of the generated images. Through this adversarial training process, the generator learns to improve its outputs iteratively, leading to significant enhancements in image detail and clarity. This approach not only restores lost information but also introduces finer textures and features, making it particularly effective for applications like medical imaging, satellite imagery, and artistic enhancement. The flexibility and efficiency of GANs enable them to adapt to various image types and resolutions, showcasing their potential in advancing image processing technologies.