DSD-GAN: Double-Scale Discriminators GAN for Enhanced Chinese Brush Baimiao Painting Colorization

Xiaoyang Bi, Yingqian Zhang, Sim Kuan Goh

Abstract: Transforming Baimiao sketches into full-color Chinese brush paintings is significant in the context of rising market demands and exploring new possibilities in the realm of AI art creation. While several pixel-based works demonstrated the capability of generative adversarial network (GAN) for colorization, several challenges remain, e.g., the inappropriate thickness of lines in sketches and the shortage of local detail. To address the challenges, this paper introduces the double-scale discriminator GAN (DSD-GAN), with double discriminators that target realistic image generation at two scales. The generator of DSD-GAN integrates a Unet-based architecture with a convolutional attention module capable of focusing on critical areas to enhance the painting color and detail accuracy. Moreover, an additional enhancement block, inspired by the perceptual field model, is adopted to improve the precision of local details in the generated artwork. Extensive verification on a diverse dataset of Chinese brush paintings, including two artistic subject matter still life and landscape, demonstrates DSD-GAN’s superiority over state-of-the-art Image-to-Image translation methods across various metrics, including PSNR, SSIM, MSE, and PI. Qualitative visual assessments further confirm its effectiveness. The source code is accessible on GitHub upon paper acceptance.