![]() ![]() Therefore, we name our framework Hierarchical Painter, which not only restores the missing objects but also generates vivid strokes that represent the painter’s style. Our main idea is to hierarchically restore the missing parts, from object structure to stroke details. To address these challenges, we proposed an inpainting model for Chinese landscape paintings, especially concentrating on the restoration of the masterpiece Dwelling in the Fuchun Mountains, one of the most well-known classical Chinese landscape paintings by the early 14th century master Huang Gongwang (1269-1354). Finally, there are usually very few surviving works of a given artist, therefore, learning their unique artistic style from limited data is a formidable challenge. Second, Chinese landscape paintings are typically created using barely black ink, with brushstrokes determined by the ink’s intensity changes, which can be challenging for models to learn. First, Chinese landscape paintings often comprise intricate natural scenes, such as trees and rocks, and thus generating missing regions based solely on the remaining parts of an image may lead to nonsensical results or disrupt the overall coherence of the painting. However, restoring Chinese landscape paintings presents three significant challenges that current inpainting approaches cannot directly address. Image inpainting, which aims to fill in missing parts of images, is a typical method used to restore 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. 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. 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. This approach enables the model to decompose the inpainting process into two separate steps, generating less informative backgrounds and more detailed foregrounds. The proposed method leverages an image processing algorithm to extract the structural information of 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. However, current inpainting models often struggle to learn the specific painting styles and fine-grained brushstrokes of individual artists when restoring Chinese landscape paintings. Image inpainting is a critical area of research in computer vision with a broad range of applications, including image restoration and editing. ![]()
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