InkGAN: Generative Adversarial Networks for Ink-And-Wash Style Transfer of Photographs

Abstract

In this work, we present a novel approach for Chinese Ink-and-Wash style transfer using a GAN structure. The proposed method incorporates a specially designed smooth loss tailored for this style transfer task, and an end-to-end framework that seamlessly integrates various components for efficient and effective image style transferring. To demonstrate the superiority of our approach, comparative results against other popular style transfer methods such as CycleGAN is presented. The experimentation showcased the notable improvements achieved with our proposed method in terms of preserving the intricate details and capturing the essence of the Chinese Ink-and-Wash style. Furthermore, an ablation study is conducted to evaluate the effectiveness of each loss component in our framework. We conclude in the end and anticipate that our findings will inspire further advancements in this domain and foster new avenues for artistic expression in the digital realm.