An Enhanced Image Colorization using Modified Generative Adversarial Networks with Pix2Pix Method

Abstract

The science of deep learning has undergone a revolution thanks to the invention of generative adversarial networks (GANs), a kind of deep learning design that can produce synthetic data that is equivalent to real-world data. GANs have recently been employed for a number of images processing operations, including photo colorization. The storing of grayscale photos, which require less storage space and can then be colored, and the restoration of old photographs are two of the key uses for image colorization. Although there are numerous color choices that can be employed with a specific grayscale image, this problem is challenging. Deep learning has been used in recent advances to make an effort to overcome this issue. In addition to the generator network, the Pix2Pix architecture also has a discriminator network that tries to tell bogus from real color images. Many datasets, including the CIFAR-10 and COCO datasets, have shown that the suggested method produces high-quality colorized images that closely resemble the ground truth images. Overall, this study shows how well GANs–and specifically the Pix2Pix architecture–can be used to colorize images. According to the research, this technology might be applied to a number of activities, including the processing of images and videos, digital restoration, and aesthetic representation.