An inverse halftoning method for various types of halftone images based on multi-scale generative adversarial network

The inverse halftoning method based on deep neural network has been widely studied in last few years. However, the existing methods only apply single-size convolution kernel to extract image features and train the proposed model with only one-scale loss function, which leads to blurring details and residual halftone noise patterns in the restored continuous-tone image. To alleviate these problems, this paper proposes an inverse halftone method for various types of halftone images based on multi-scale generative adversarial network. In the generative model, an elaborated multi-scale feature extraction model is firstly designed to extract halftone image features in parallel, so as to obtain more comprehensive image information from the input halftone image and promote the effective fusion of the extracted features. Moreover, a detail enhancement subnetwork is employed to fine-tune the image details. In adversarial model, a multi-scale discriminator is used to further enhance the image details by learning the distribution characteristics of image information. The experiments on different types of halftone images show that the proposed method significantly outperforms the existing state-of-art methods.