Generative Adversial Network Approach for Cartoonifying image using CartoonGAN

Abstract

This research paper aims to discuss the conversion of images into cartoons using NPR algorithms. The goal of NPR is to create images that appear to have been produced by traditional artistic media such as painting, drawing, or cartoons. In this paper, we discuss the NPR algorithm used for converting images into cartoons, including edge detection, color simplification, and shading techniques also discuss the advantages and limitations of the NPR algorithm for cartoon image conversion. The proposed method consists of three main steps: training a GAN model, generating cartoon images using the trained GAN model, and stylizing the generated cartoon images using K-means clustering algorithm. In the first step, a GAN model is trained on a dataset of real images and corresponding cartoon images. The generator network of the GAN model takes a noise vector as input and generates a cartoon image. The GAN model is trained using adversarial loss, which encourages the generator network to generate cartoon images that are similar to the corresponding real images. In the second step, the trained GAN model is used to generate cartoon versions of real images. The generated cartoon images are then segmented into regions using K-means clustering algorithm. The segmented regions are then stylized using the colors from the corresponding clusters. In the third step, the stylized regions are combined to generate the final cartoon image. The proposed method is evaluated on a dataset of real images and corresponding cartoon images. The results show that the proposed method outperforms the state-of-the-art methods regarding visual quality and quantitative metrics. The visual quality of the generated cartoon images is also evaluated using human perception studies, which shows that the proposed method produces cartoon images that are visually appealing and similar to the corresponding real images.