Image colourization using GANs algorithm

Image colorization using Generative Adversarial Networks (GANs) is a sophisticated technique that enhances grayscale images by predicting and adding realistic colors. This process involves two neural networks: the generator and the discriminator. The generator creates colorized images from grayscale inputs, while the discriminator evaluates these images against real colored examples, providing feedback to the generator. Through iterative training, the generator improves its ability to produce lifelike colors that are contextually appropriate, resulting in more vibrant and accurate representations. GANs leverage vast datasets of color images to learn associations between shades and objects, allowing for nuanced and visually appealing colorizations that maintain the integrity of the original grayscale images. This approach has applications in various fields, including art restoration, media enhancement, and machine learning research.