<![CDATA[This research proposes the use of a Generative Adversarial Network (GAN), a deep learning approach consisting of two neural networks: a generator that generates high-resolution images from low-resolution images, and a discriminator that distinguishes between original high-resolution images. and the image the generator produces. Through joint training, the generator learns to produce increasingly realistic and detailed images. This research uses training data of 400 image data, 100 images consisting of training data and test data. The GAN model trial showed a success rate of 80% training data, 20% test data. This process continued through repeated testing and 10,000 epoch training periods using Pytorch to train the GAN, with sharper and more detailed results than conventional methods. The application of GANs in various applications such as medical image processing, video restoration, and security surveillance shows great potential in improving image quality. Challenges such as training stability and computational time are overcome through more efficient regularization and optimization techniques, so that GANs prove to be a powerful tool for image resolution enhancement with a significant contribution to the development of more advanced image processing technologies.]]>
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