SGCGAN: A Style-Guided Colorization Network for Traditional Chinese Landscape Paintings with Grayscale Sketch

Automatic sketch colorization is a challenging task in both computer graphics and computer vision since all the color, texture, and shading generation must be created based on the abstract sketch. Moreover, historical degradation factors-including pigment fading and physical erosion-have severely compromised the original chromatic and structural integrity of traditional Chinese paintings, introducing unique challenges for digital restoration and recolorization. Previous methods often yielded unsatisfactory results when dealing with Chinese paintings. To address this issue, we propose a Style-Guided Colorization Generative Adversarial Network for Traditional Chinese landscape paintings, termed SGCGAN. Specifically, we propose a novel AdaCAttN network that can more accurately learn the content and style features of both Grayscale Sketch and Color Painting. Moreover, we conducted hybrid training using both paired and unpaired datasets, utilizing the unpaired data to increase the generalization ability of the model, and using the paired data to guide the learning process of the model. Then, we introduce a Color Histogram Structural Similarity Loss to preserve chromatic distribution patterns during the colorization process. The color style constraint is realized by measuring the difference in color distribution between the source image and the target image. More importantly, we innovatively extract the Grayscale Sketch of traditional Chinese landscape paintings, which not only retains part of the grayscale information but also visually matches the characteristics of white landscape painting. We create a Chinese Landscape Painting dataset with Grayscale Sketch named SketchCLP. Extensive experiments and ablation studies demonstrate the superiority of the proposed method over previous state-of-the-art methods. The code is available at https://github.com/LPDLG/SketchCLP.