Synthesizing Images from Hand-Drawn Sketches using Conditional Generative Adversarial Networks
Bipin Kuriakose, Theres Thomas, Nikitha Elsa Thomas, Sharon John Varghese, Veena A. Kumar · 2020 International Conference on Electronics and Sustainable Communication Systems (ICESC) · 2020
Today, Technology have remarkable charm in the area of Computer Graphics and Vision. Producing absolute images from the poor hand-drawn sketches is a very demanding and laborious task in this area. Hand-drawn sketch recognition is widely used in sketch based image and video retrieval, manipulations and reorganizations. In S ketch to image synthesis, the sketches are translated to realistic images with the use of a generative model. An image is put forward to image translation network that involves in producing a synthesized image from the input sketch via an adversarial process. A novel Conditional Generative Adversarial Network (cGANs) which is an extension of Generative Adversarial Networks (GANs) is used to produce the images with some sort of conditions or attributes. In this work, the implementation of cGANs for synthesizing the images from hand-drawn sketches gives a remarkable output. The performance of the proposed sketch to image translation network was excellent and appreciable.