Elevating Images to Vibrant Realism Through Conditional GAN Colorization

Image colorization elevates the aesthetic charm by infusing vibrant hues into black-and-white images, creating a more immersive and engaging viewing experience across various contexts, from photography to film and digital media. It plays a pivotal role in modernizing and revitalizing visual content, impacting audience perception. The study navigates the domain of image colorization, emphasizing grayscale images using Conditional Generative Adversarial Networks(CGANs) for visual restoration. By seamlessly integrating technologies like U-Net and PatchGAN, it tackles the challenge of creating visually appealing and contextually coherent colorizations. The methodology employs CGANs with performance metrics defined by a set of equations, ensuring accurate colorizations. Results showcase the efficacy of CGANs in producing lifelike images, setting new benchmarks in practical image colorization. The proposed architecture incorporates a Generative Adversarial Network (GAN) Loss with a measured value of 1.90, showcasing its robust performance in generating compelling results.