  In the present study, we propose to implement a new framework for estimating
generative models via an adversarial process to extend an existing GAN
framework and develop a white-box controllable image cartoonization, which can
generate high-quality cartooned images/videos from real-world photos and
videos. The learning purposes of our system are based on three distinct
representations: surface representation, structure representation, and texture
representation. The surface representation refers to the smooth surface of the
images. The structure representation relates to the sparse colour blocks and
compresses generic content. The texture representation shows the texture,
curves, and features in cartoon images. Generative Adversarial Network (GAN)
framework decomposes the images into different representations and learns from
them to generate cartoon images. This decomposition makes the framework more
controllable and flexible which allows users to make changes based on the
required output. This approach overcomes any previous system in terms of
maintaining clarity, colours, textures, shapes of images yet showing the
characteristics of cartoon images.
