Hierarchical painter: Chinese landscape painting restoration with fine-grained styles

Abstract

Image inpainting is a critical area of research in computer vision with a broad range of applications, including image restoration and editing. However, current inpainting models often struggle to learn the specific painting styles and fine-grained brushstrokes of individual artists when restoring Chinese landscape paintings. To address this challenge, this paper proposes a novel inpainting model specifically designed for Chinese landscape paintings, featuring a hierarchical structure that can be applied to restore the famous Dwelling in the Fuchun Mountains with remarkable fidelity. The proposed method leverages an image processing algorithm to extract the structural information of Chinese landscape paintings. This approach enables the model to decompose the inpainting process into two separate steps, generating less informative backgrounds and more detailed foregrounds. By seamlessly merging the generated results with the remaining portions of the original work, the proposed method can faithfully restore Chinese landscape paintings while preserving their rich details and fine-grained styles. Overall, the results of this study demonstrate that the proposed method represents a significant step forward in the field of image inpainting, particularly for the restoration of Chinese landscape paintings. The hierarchical structure and image processing algorithm used in this model is able to faithfully restore delicate and intricate details of these paintings, making it a promising tool for art restoration professionals and researchers.