Abstract
AbstractHalftoning is a method of quantifying a continuous tone image into a binary image. Halftone methods can be categorized into dithering, error diffusion and iterative method. We present a new method different from traditional halftoning algorithms for learning the mapping between continuous images and halftone images using conditional generative adversarial networks (conditional GANs). We regard halftoning and inverse halftoning as the process of image-to-image translation, and use the classic pix2pixHD network. In this work, we use multi-scale generator and discriminator architectures to perform both halftoning and its structural reconstruction. The experimental results show that this method can better fit some classical halftoning algorithms to realize halftoning and inverse halftoning. Compared with the existing methods, our method can better realize reconstruction of halftone images.KeywordsHalftoningInverse HalftoningGAN
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
