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Ancient murals, invaluable cultural artifacts, frequently suffer damage from environmental and human factors, necessitating effective restoration techniques. Traditional methods, which rely on manual skills, are time-consuming and often inconsistent. This study introduces an innovative mural restoration approach using a generative adversarial network (GAN) within a UNet architecture. The generator integrates Transformer and convolutional neural network (CNN) components, effectively capturing and reconstructing complex mural features. This work's novelty lies in integrating the Group-wise Multi-scale Self-Attention (GMSA), an Encoder-Decoder Feature Interaction (EDFI) module, and a Local Feature Enhancement Block (LFEB). These components allow the model to better capture, reconstruct, and enhance mural features, leading to a significant improvement over traditional restoration methods. Tested on a dataset of Tang Dynasty murals, the method demonstrated superior performance in PSNR, SSIM, and LPIPS metrics compared to seven other techniques. Ablation studies confirmed the effectiveness of the heterogeneous network design and the critical contributions of the GMSA, EDFI, and LFEB modules. Practical restoration experiments showed the method's ability to handle various types of mural damage, providing seamless and visually authentic restorations. This novel approach offers a promising solution for the digital preservation and restoration of cultural heritage murals, with potential applications in practical restoration projects. [ABSTRACT FROM AUTHOR]

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Due to the limitations of a priori knowledge and convolution operation, the existing image restoration techniques cannot be directly applied to the cultural relics mural restoration, in order to more accurately restore the original appearance of the cultural relics mural images, an image restoration based on the denoising diffusion probability model (Denoising Diffusion Probability Model (DDPM)) and the Transformer method. The process involves two steps: in the first step, the damaged mural image is firstly utilized as the condition to generate the noise image, using the time, condition and noise image patch as the inputs to the noise prediction network, capturing the global dependencies in the input sequence through the multi-attention mechanism of the input sequence and feed-forward neural network processing, and designing a long skip connection between the shallow and deep layers in the Transformer blocks between the shallow and deep layers using long skip connections to fuse the feature information of global and local outputs to maintain the overall consistency of the restoration results; In the second step, taking the noisy image as a condition to direct the diffusion model to back sample to generate the restored image. Experiment results show that the PSNR and SSIM of the proposed method are improved by 2% to 9% and 1% to 3.3%, respectively, which are compared to the comparison methods. This study proposed synthesizes the advantages of the diffusion model and deep learning model to make the mural restoration results more accurate. [ABSTRACT FROM AUTHOR]

Abstract Ancient murals are precious cultural heritages. They suffer from various damages due to man-made destruction and long-time exposure to the environment. It is urgent to protect and restore the damaged ancient murals. Virtual restoration of ancient murals aims to fill damaged mural regions by using modern computer techniques. Most existing restoration approaches fail to fill the loss mural regions with rich details and complex structures. In this paper, we propose a virtual restoration network of ancient murals based on global–local feature extraction and structural information guidance (GLSI). The proposed network consists of two major sub-networks: the structural information generator (SIG) and the image content generator (ICG). In the first sub-network, SIG can predict the structural information and the coarse contents for the missing mural regions. In the second sub-network, ICG utilizes the predicted structural information and the coarse contents to generate the refined image contents for the missing mural regions. Moreover, we design an innovative BranchBlock module that can effectively extract and integrate the local and global features. We introduce a Fast Fourier Convolution (FFC) to improve the color restoration for the missing mural regions. We conduct experiments over simulated and real damaged murals. Experimental results show that our proposed method outperforms other three comparative state-of-the-art approaches in terms of structural continuity, color harmony and visual rationality of the restored mural images. In addition, the mural restoration results of our method can achieve comparatively high quantitative evaluation metrics.

Ancient murals are precious cultural heritages. They suffer from various damages due to man-made destruction and long-time exposure to the environment. It is urgent to protect and restore the damaged ancient murals. Virtual restoration of ancient murals aims to fill damaged mural regions by using modern computer techniques. Most existing restoration approaches fail to fill the loss mural regions with rich details and complex structures. In this paper, we propose a virtual restoration network of ancient murals based on global–local feature extraction and structural information guidance (GLSI). The proposed network consists of two major sub-networks: the structural information generator (SIG) and the image content generator (ICG). In the first sub-network, SIG can predict the structural information and the coarse contents for the missing mural regions. In the second sub-network, ICG utilizes the predicted structural information and the coarse contents to generate the refined image contents for the missing mural regions. Moreover, we design an innovative BranchBlock module that can effectively extract and integrate the local and global features. We introduce a Fast Fourier Convolution (FFC) to improve the color restoration for the missing mural regions. We conduct experiments over simulated and real damaged murals. Experimental results show that our proposed method outperforms other three comparative state-of-the-art approaches in terms of structural continuity, color harmony and visual rationality of the restored mural images. In addition, the mural restoration results of our method can achieve comparatively high quantitative evaluation metrics. [ABSTRACT FROM AUTHOR]

Abstract Mural is an important component of culture and art of Dunhuang in China. Unfortunately, these murals had been ruined or are being ruined by some diseases such as cracking, hollowing, falling off, mildew, dirt, and so on. Existing image restoration algorithms have problems such as incomplete repair and disharmonious texture during large-area repair, so the effect of mural image disease area repair is poor. Due to lack of a standard mural datasets, Dunhuang mural datasets are created in the paper. Meanwhile, our network architecture SeparaFill is proposed which connects two generators based on U-Net. Based on the characteristics of the painting, the contour line pixel area of the mural image is innovatively separated from the content pixel area. Firstly, the contour restoration generator network with skip connect and hierarchical residual blocks is employed to repair contour lines. Then, the color mural image is repaired by the content completion network with guide of the repaired contour. Full resolution branches and generator branches of the U type are exploited in content completion generators. Convolution layers of different kernel sizes are fused to improve the reusability of the underlying features. Finally, global and local discriminant networks are applied to determine whether the repaired mural image is authentic in terms of both the modified and unmodified areas. The proposed SeparaFill shows good performance in restoring the line structure of the damaged mural images and retaining the contour information of the mural images. Compared with existing restoration algorithms in mural real damage repair experiment, our algorithm increases the peak signal-to-noise ratio (PSNR) by an average of 1.1–4.3 dB and the structural similarity (SSIM) values were slightly improved. Experimental results reveal the good performance of the proposed model, which can contribute to digital restorations of ancient murals.

Mural is an important component of culture and art of Dunhuang in China. Unfortunately, these murals had been ruined or are being ruined by some diseases such as cracking, hollowing, falling off, mildew, dirt, and so on. Existing image restoration algorithms have problems such as incomplete repair and disharmonious texture during large-area repair, so the effect of mural image disease area repair is poor. Due to lack of a standard mural datasets, Dunhuang mural datasets are created in the paper. Meanwhile, our network architecture SeparaFill is proposed which connects two generators based on U-Net. Based on the characteristics of the painting, the contour line pixel area of the mural image is innovatively separated from the content pixel area. Firstly, the contour restoration generator network with skip connect and hierarchical residual blocks is employed to repair contour lines. Then, the color mural image is repaired by the content completion network with guide of the repaired contour. Full resolution branches and generator branches of the U type are exploited in content completion generators. Convolution layers of different kernel sizes are fused to improve the reusability of the underlying features. Finally, global and local discriminant networks are applied to determine whether the repaired mural image is authentic in terms of both the modified and unmodified areas. The proposed SeparaFill shows good performance in restoring the line structure of the damaged mural images and retaining the contour information of the mural images. Compared with existing restoration algorithms in mural real damage repair experiment, our algorithm increases the peak signal-to-noise ratio (PSNR) by an average of 1.1–4.3 dB and the structural similarity (SSIM) values were slightly improved. Experimental results reveal the good performance of the proposed model, which can contribute to digital restorations of ancient murals. [ABSTRACT FROM AUTHOR]

The Editor-in-Chief and the publisher have retracted this article, which was submitted as part of a guest-edited special section. An investigation uncovered evidence of systematic manipulation of the publication process, including compromised peer review. The Editor and publisher no longer have confidence in the results and conclusions of the article. GW, YL, and NW either did not respond directly or could not be reached. [ABSTRACT FROM AUTHOR]