In this work, we propose a two-stage architecture to perform image inpainting from coarse to fine. The framework extracts advantages from different designs in the literature and integrates them into the inpainting network. We apply region normalization to generate coarse blur results with the correct structure. Then, contextual attention is applied to utilize the texture information of background regions to generate the final result. Although using region normalization can improve the performance and quality of the network, it often results in visible color shifts. To solve this problem, we introduce perceptual color distance in the loss function. In quantitative comparison experiments, the proposed method is superior to the existing similar methods in Inception Score, Fréchet Inception Distance, and perceptual color distance. In qualitative comparison experiments, the proposed method can effectively resolve the problem of color shifts.
- Deep learning
- Generative adversarial networks
- Image inpainting
- Image processing