Cognitive Chromatic Image Synthesis Using UNET and GAN

In the last decade, there has been a lot of interest for image colorization over a wide range of applications, especially in the restoration of old or damaged images. Because there are a lot of options when it comes to assigning color information, this problem is ill-posed by nature and is quite difficult to solve. Researchers have handled this issue in a variety of imaginative ways. More recent developments in automated colorization are focused on images that are repetitive in nature or images that require extensive editing. For instance, in such settings, semantic maps can be used as additional input to offer better control over the generalization of the colorization task with the help of conditional Deep Convolutional Generative Adversarial Networks (DCGANs).Our solution combines the techniques to allow computers to produce vivid visuals in this way. Monochrome or black and white images most of the times differ from the colored images in terms of visual detail and image content and colorizing them by hand is a tedious and often an artistic task.