CartoonifyGAN: Generative Adversarial Networks for Image Cartoonization

In this paper, a solution forconverting real-world pictures into cartoonified pictures that is both useful and exciting in computer vision is proposed. Our method is organised as a knowledge-based plan that has recently gained popularity as a method of stylizing images in creative forms such as painting. Existing artistic style methods on the other hand do not produce satisfactory results because (1) cartoon styles have distinct features such as elite resolution and generalizability (2)cartoon images have smooth edges with obvious color changes. In this paper, it provide cartoonifyGAN,a Generative Adversarial Network (GAN ) methodology for cartoonization. It utilize mismatched photographs and hilarious images for teaching cartoonization which is a simple process and makes excellent cartoon drawings from real-world pictures