Investigation of Different Generative Adversarial Networks Techniques for Image Restoration

Generative Adversarial Networks are artificial neural networks that pit two different sets of neural networks against one another in order to generate data that isn't part of the training set.The Generative Adversarial Network (GAN) produces good outcomes when they are trained on image data that comes from the actual world.The generator and the discriminator make up the Generative Adversarial Network (GAN), which stands for "generative adversarial network."The parameters that were utilized to generate the data are completely arbitrary.The information is evaluated, and erroneous information is distinguished from true information by the discriminator.GAN has proven to be useful in various domains, such as object recognition, text synthesis, face ageing, image manipulation, image overpainting, image stitching, human pose synthesis, visual salience prediction, stenographic applications, and many more.Several researchers have investigated various types of GANs but comprehensive analysis and comparison of different types of recent GAN's like Deep Convolutional GAN, Wasserstein GAN,Auto Enocoder, Cycle GAN,Progressive GAN and Super Resoultion GAN have not been published so far in the literature.In this article, quantitative and qualitative analysis are carried on to evaluate their suitability of the GAN for a particular application.Examples of applications encompass texture production, facial reconstruction, facial recognition, high-resolution imaging, music composition, drawing creation, cosmetic enhancements, image transformation, voice synthesis, medical diagnostics, and video editing.No single GAN cannot fulfil the desired requirements for all the applications.The article concludes with a discussion of the possible uses of GANs as well as how these applications constitute a fascinating new area of research and prospective expansion.