International Journal of Advanced Research in Computer and Communication Engineering

Abstract: Transforming an image from one form to another is nothing but image-style translation. The image transformation plays a key role in computer vision and digital media, especially in transforming real-world images to artistic styles such as cartoons. Generally these conversions can be done manually by artists; however, advancements in technology like image editing tools have made it easy. But, most of them are time-consuming and do not ensure that the originality of the image is preserved. Our project“A Generative Adversarial Network-Based Framework for Photorealistic -to-Cartoon Image Style Translation” focuses on generating the quality image translation, in addition to preserving the originality of the image. In this model we are using a CycleGAN, a deep learning framework which consists of a generator for generating the real to cartoon images and a discriminator for evaluating the generated output. The system mainly focuses on simplifying image textures, smoothening the surfaces and broadening the edges for cartoon-like appearance. The results of the system successfully generates the cartoon- style images that preserves the important features of the original image.This image transformation framework can be applied in areas such as digital art creation, animation preprocessing, social media filters, and creative design tools.

Index Terms: Generative Adversarial Networks (GAN), Image Style Translation, Cartoon Image Generation, Photorealistic Images, Computer Vision, Deep Learning, Automated Image Transformation.

Downloads: | DOI: 10.17148/IJARCCE.2026.15241