Descreening of dispersed-dot halftone images with generative adversarial network

Abstract

Halftoning a continuous-tone image inherently results in loss of information, which makes the inverse process, descreening, a challenging problem. Current state-of-the-art descreening algorithms have two issues: first, they mostly are PSNR-oriented reconstruction algorithms, which tend to generate piecewise smooth images that do not appear realistic due to their lack of texture. Furthermore, these algorithms are typically trained with halftone images generated from the Floyd-Steinberg error diffusion algorithm, which is not an optimal choice since the algorithm is known to generate visible artifacts in the halftone image. We address these issues by the following: first, we propose a new descreening algorithm based on conditional generative adversarial networks (cGAN) that generate descreened images with abundant texture resulting in more realistic appearance. Next, we propose using the direct binary search (DBS) algorithm instead of Floyd-Steinberg error diffusion for generating the halftone images, since it is known to generate halftone images without visible artifacts. Both qualitative and quantitative comparisons show that our algorithm outperforms state-of-the-art descreening algorithms significantly.