Pix2pix GAN for Image‐to‐Image Translation
B. Lakshmipriya, Pankaj Kumar Singh, S. Jayalakshmy · 2025
Image-to-image translation (I2IT) is a complex task involving the transformation of images from one domain to another while retaining their essential content. With the onset of generative adversarial networks (GANs), image translation has attracted interest recently as it can synthetically generate images belonging to a new domain or scene from the images available in another domain. I2IT essentially learns the mapping of patterns from images in one domain to another domain and finds a wider application ranging from satellite images to medical image enhancement and autonomous driving simulations. Pix2pix GAN is a groundbreaking I2IT architecture that has completely changed how realistic images are produced from input data. This chapter examines the effectiveness of pix2pix GAN in producing high-quality output images from a variety of datasets, including urban planning, medical imaging, and underwater image enhancement. Through an analysis of pix2pix GAN's performance on these datasets, the project aims to offer important insights into the technology's advantages, disadvantages, and possible uses in a variety of fields. In addition, the project will investigate ways to improve the scalability and performance of pix2pix GAN, increasing its usefulness for I2IT tasks in the real world. Pix2pix GAN employs a generator to translate input images into realistic outputs and a discriminator to contradistinguish the synthetic images from the actual and utilize the paired image datasets for training employing adversarial and L1 loss functions to optimize generator and discriminator networks iteratively, ensuring high-quality image translation. A comparative study for the performance of pix2pix GAN architecture on three datasets are as follows: (i) satellite aerial images to map generation from Kaggle pix2pix dataset and (ii) underwater image enhancement using large-scale underwater image (LSUI) dataset and magnetic resonance imaging (MRI) to synthetic computed tomography (CT) image generation.