Twin Progressive Generative Adversarial Network For High-Resolution Image Inpainting

Zhiying Li, Weibin Chen, Zhaoxin Fan, Kaichuan Kong, Xiaobo Jin, Guanggang Geng

Abstract: Image inpainting aims to generate content for missing regions while maintaining visual coherence in the reconstructed images. Generative Adversarial Networks (GANs) have received increasing attention for their ability to repair high-resolution images. However, existing methods often overly rely on surrounding pixel information, overlooking noisy or incomplete information in boundary regions, which leads to blurred and unnatural results. To address these limitations, we propose a Twin Progressive Generative Adversarial Network (TP-GAN), which leverages global visual features from distant image contexts to reconstruct the overall structure and texture, improving the quality of high-resolution image inpainting. TP-GAN incorporates two generators and one discriminator, where the generators collaborate via exponential moving average optimization, focusing respectively on capturing fine details and global information. A progressive learning strategy is employed, starting with low-resolution restoration and gradually increasing resolution to simplify tasks and enhance adaptability to boundary regions. Extensive experimental evaluations on popular datasets demonstrate the superiority of TP-GAN.

External IDs:dblp:conf/icmcs/LiCFKJG25