Mural inpainting progressive generative adversarial networks based on structure guided

Aiming at the problems of improper structural repair and loss of mural detail reconstruction after repairing during the process of damaged mural image inpainting, mural inpainting progressive generative adversarial networks based on structure guided is proposed. Firstly, a structure generator is designed to generate the missing structure content of the mural. Secondly, the mural generator is used to generate adversarial learning, and combined with the improved double pooling SKNet multi-scale feature extraction modular, the repaired structure image is used to guide the damaged mural to achieve progressive repair, which improves the detailed feature learning ability of the mural. Lastly, the reconstruction of the structural picture and the mural image is finished using the local discriminator and the global discriminator, which improves the overall consistency of the mural restoration result. Experiments on digital restoration of real Dunhuang murals show that the proposed method can effectively repair damaged Dunhuang murals, and the restored murals have a stronger structure and high-quality texture details than other comparison algorithms. Meanwhile, the proposed has better both subjective and objective evaluation. © 2023 Beijing University of Aeronautics and Astronautics (BUAA). All rights reserved.