Sketches to Realistic Images: A Multi-GAN based Image Generation System
Shailaja Nilesh Uke, Om Surase, Tilak Solunke, Siddhesh Marne, Soham Phadke · 2024
In the current era of generative Artificial Intelligence (AI) boom, most image generation models rely solely on prompt-based input to generate images. Often it is very challenging for a user to convey their ideas using textual prompts, thus resulting in images that differ significantly from user's intended vision. The proposed AI image generation system enables users to input a rough sketch, which is then transformed into a refined and accurate image closely aligned with the original sketch. This is achieved by integration of multiple state of art Generative Adversarial Networks (GANs) such as pix2pix (image translation), Deep Convolutional Generative Adversarial Network (DC GAN), Enhanced Super-Resolution Generative Adversarial Networks (ESRGANs) (image restoration). This AI system makes image generation tasks more intuitive and aligned with user expectations. This method could improve design processes in fields like fashion and architecture.