H-GAN: Deep Learning Model for Halftoning and its Reconstruction

Digital halftone deals with transforming a gray scale image into its printable binary version. In this paper, a generative adversarial network-based model is proposed to perform both halftoning and its structural reconstruction. The GAN model is based on the concept of unpaired image to image translation and the model learns both transformations simultaneously. For optimal training, the model is feed with high structural data and the model architechture is also modified in accordance to this transformation problem. From results, it has been validated that the model performs with consistent accuracy in both transformations.