Image-To-Image Translation Using Pix2Pix GAN and Cycle GAN

Image-to-image (I2I) translation helps to generate new images based on existing ones, allowing for the creation of new art, designs, and objects. It is used for various tasks such as creating photorealistic images from sketches, converting aerial photographs to street maps, colorizing black and white images, and generating high-resolution images. Using image translation, we can use data more efficiently, reduce the amount of manual labor needed to annotate data, and increase the accuracy of data-driven models. Therefore, this research study intends to develop a model and make it applicable to various problems with the similar generic approach. The proposed model translates images from one domain to another using Generative Adversarial Networks (GAN). At first, Pix2Pix GAN is used to perform image translation tasks. However, Pix2Pix GAN needs target images (paired datasets) for transition. Cycle GAN does the image translation tasks using unpaired datasets. This research work shows simulation analysis of image translation task of two GAN models.