Image segmentation for content-color-dependent screening (CCDS) using U-net

Abstract

In this work, we propose to use deep learning to segment an image based on its color and its content. We start by using the content‑color‑dependent screening (CCDS) developed previously. The goal of CCDS is to apply different color assignments for the two or more regular or irregular halftones within the image depending on the local color and content of the image. The main drawback of the proposed approach is that for a given image, the result highly depends on the initial parameters, such as the number of clusters, low and high thresholds for edge detection, bilateral filter parameters and others. In this work, we propose to use the well‑known U‑net architecture to detect the smooth areas of the image. U‑net is a type of a convolutional neural network (CNN) designed for fast, accurate image segmentation, and it is used to predict a label for every single pixel.