Analysis of Pix2Pix and CycleGAN for Image-to-Image Translation: A Comparative Study

The primary objective of image-to-image translation technology is to transfer picture styles and characteristics from one image domain to another; this area of study is now dominating the computer vision research landscape. Image translation has seen significant improvements in performance thanks to the fast development of convolutional neural networks, particularly generative adversarial networks. This has led to widespread applications in many domains, including photo synthesis, object reconstruction from line drawings, and picture coloring. In its most basic form, the Image-to-Image Translation issue is a mapping from one image to another. When it comes to dealing with various translation tasks, the performance of various general frameworks can vary substantially, limited by task-specific variables. To address these challenges, this study compares and evaluates the advantages of two prominent and advanced translations of images to image structures, Pix2Pix and CycleGAN, from a variety of perspectives, covering network architecture, loss function programs, and model correctness. Furthermore, we analyze solutions developed from these two exemplary systems and present the results of translation of image processing for different real-world application scenarios. Finally, we review the current issues and outline potential avenues for further research.