Digital image inpainting is also a hot issue in computer vision, aiming to fill in missing areas in the image. Digital inpainting makes this problem a solution. Traditional mural inpainting uses manual inpainting, which is irreversible and may result in secondary damage. However, the large areas of detachment and salt efflorescence are difficult to solve. The cracks and small amounts of detachment are easy to solve. Many typical diseases are associated with tomb murals, such as cracks in the murals large areas of the tomb with hollow drums causing the images to peel off pigment layer warping and peeling salt efflorescence and mold in the murals artificial excavation. The surviving murals in the burial tomb are scarce, with little information on the various styles, and are diseased and challenging to inpaint. Being buried deep underground, they have been damaged by natural factors and human excavation. The tomb murals were generally painted on the mudbrick walls of the burial tombs and suffered from the difficulties of excavation, uncovering them, and the fact that they were easily oxidized after uncovering and could not be easily preserved. The murals in the burial tombs reflect the humanistic and technological development of the time and are of excellent research significance. It can be used as a reference for experts in manual inpainting, saving the cost and time of manual inpainting. It demonstrates the performance of this paper in inpainting different diseases of murals. Finally, the segmental loss function and its training method are improved.The experimental results show that the results of using signal-to-noise ratio (PSNR), structural similarity (SSIM), and mean square error (MSE) on epitaxial mask, crack mask, random small mask, and random large mask are better than other methods. Secondly, the model combines spatial and channel attention with multiscale feature aggregation to change the mapping network structure and improve the inpainting accuracy. Firstly, an improved residual prior and attention mechanism is added to the generator module to preserve the image structure. Therefore, this paper presents an inpainting model based on dual-attention multiscale feature aggregation and an improved generator. The generative adversarial network is, recently, a more effective method. Traditional deep learning inpainting causes information loss and generates irrational structures. Due to the scarcity of samples and the variety of damage, the image features are scattered and partially sparse, and the colors are less vivid than in other images. Therefore, the need for digital inpainting is increasing to save time and costs. Traditional mural inpainting takes a long time and requires experts to draw it manually. As the only underground mural in the collection, the tomb murals are subject to damage due to temperature, humidity, and foundation settlement changes.
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