User-Guided Chinese Painting Completion–A Generative Adversarial Network Approach

Abstract

Image completion models based on deep neural networks have been a research hot spot in computer vision. However, most of the previous methods focus on natural images, such as faces and landscapes. In this paper, we propose a novel image completion model for a special set of artificial ancient Chinese paintings to address this limitation. Specifically, we integrate three complements: the Wasserstein Generative Adversarial Networks (WGAN), Perceptual loss, and Mean Squared Error (MSE) to train the model robustly. We propose a unique generator which can not only pay more attention to complete the details of ancient Chinese paintings but also can provide the synthesized lines to help artists to analyze paintings conveniently. Additionally, we also allow a user to supply a structure hint to guide our model to complete Chinese paintings according to his/her preference. Extensive experiments firmly demonstrate the effectiveness of our approach to complete ancient Chinese paintings and remove abnormal color blocks from them.

Teaching Chinese Painting Colour Based on Intelligent Image Processing Technology

“…As a unique type of Chinese art, Chinese painting has a long history and cultural connotation. In contemporary times, with the improvement of the national economic level, the enhancement of comprehensive national power and the return of traditional culture, Chinese painting has occupied an increasingly important position in teaching [1][2]. In today's accelerated economic globalization, the collision between Chinese and Western cultures and between traditional and modern cultures has led to many new problems, and some problems in the current teaching of Chinese painting in colleges and universities cause people to think deeply.…”