Automatic Colorization of Chinese Ink Painting Combining Multi-Level Features and Generative Adversarial Networks

Abstract

Advanced Chinese ink painting also includes work-brush flower and bird paintings with brilliant colors, in contrast to traditional ink paintings that often only use water, ink, and black and white. This serves as the foundation for our investigation into a generalized transfer problem involving ink and wash, or an ink painting coloring problem. Our goal is to automatically colorize black and white ink paintings using deep neural networks. This study can serve as a guide for coloring ink paintings and broaden the range of applications for ink painting style transfer. The high-level semantic information and low-level local features of ink paintings cannot be successfully extracted using the current generalized style transfer approach (colorization algorithm). The resulting images have muddy borders and low color saturation. In order to improve the accuracy and coherence of the coloring of ink paintings, we build training by combining the global and local features of ink paintings with the achievements of generative adversarial networks already made in the field of colorization. Comparative and objective evaluations of the experimental portion are made using metrics like peak signal-to-noise ratio (PSNR), structural similarity (SSIM), colorfulness, and user studies. Additionally, our approach beats the previous comparison approaches in terms of creative expression, color richness, and color overflow management.

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