Digital halftoning and gamut mapping for an inkjet nail printer and digital halftoning and descreening with deep learning

In this dissertation, we propose four novel digital image processing algorithms. First, we discuss a novel digital halftoning algorithm that efficiently removes halftone artifacts commonly associated with error diffusion while adding only an insignificant computational cost. Second, we propose a novel gamut mapping algorithm that utilizes the entire printer gamut resulting in more saturated print results. Third, we propose two digital halftoning algorithms using deep neural networks that generate halftones with quality comparable to those generated with the direct binary search (DBS) algorithm. Lastly, we propose a descreening algorithm based on generative adversarial networks (GAN) framework that generates images with realistic texture.