Efficient Comic Content Extraction and Coloring Composite Networks

Comics are widely loved by fans around the world as a form of visual art and cultural communication. With the development of digitalization, automated comic content detection and segmentation and comic coloring systems have become important research directions for digital archiving, automatic translation, and visual content analysis. This paper proposes a composite network composed of efficient content extraction and colorization, which includes a comic extraction module and a comic colorization module based on an improved Generative Adversarial Network. It solves the problem of single performance and poor effect that has been present in previous models. In various performance comparison experiments, our model shows an excellent and robust performance.