H-GAN: Deep Learning Model for Halftoning and its Reconstruction

被引：5

作者：

Guo, Jing-Ming ^{[1
]}

Sankarasrinivasan, S. ^{[1
]}

机构：

[1] Natl Taiwan Univ Sci & Technol, Dept Elect Engn, Taipei, Taiwan

来源：

2020 IEEE INTERNATIONAL CONFERENCE ON CONSUMER ELECTRONICS (ICCE) | 2020年

关键词：

D O I：

10.1109/icce46568.2020.9043160

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

Digital halftone deals with transforming a gray scale image into its printable binary version. In this paper, a generative adversarial network-based model is proposed to perform both halftoning and its structural reconstruction. The GAN model is based on the concept of unpaired image to image translation and the model learns both transformations simultaneously. For optimal training, the model is feed with high structural data and the model architechture is also modified in accordance to this transformation problem. From results, it has been validated that the model performs with consistent accuracy in both transformations.

引用

页码：696 / 697

页数：2

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