Ancient mural restoration based on a modified generative adversarial network

Jianfang Cao,Qi Zhang,Zibang Zhang,Hongyan Cui,Aidi Zhao

doi:10.1186/s40494-020-0355-x

Jianfang Cao, Qi Zhang + Show 3 more

Open Access

https://doi.org/10.1186/s40494-020-0355-x

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Abstract

How to effectively protect ancient murals has become an urgent and important problem. Digital image processing developments have made it possible to repair damaged murals to a certain extent. This study proposes a consistency-enhanced generative adversarial network (GAN) model to repair missing mural areas. First, the convolutional layer from a fully convolutional network (FCN) is used to extract deep image features; then, through deconvolution, the features are mapped to the size of the original image and the repaired image is output, thereby completing the regenerative network. Next, 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. In adversarial learning, the generative and discriminant network models are optimized to better complete the mural repair. The network introduces a dilated convolution that increases the convolution kernel’s receptive field. Each network convolutional layer joins in the batch standardization (BN) process to accelerate network convergence and increase the number of network layers and adopts a residual module to avoid the vanishing gradient problem and further optimizing the network. Compared with existing mural restoration algorithms, the proposed algorithm increases the peak signal-to-noise ratio (PSNR) by an average of 6–8 dB and increases the structural similarity (SSIM) index by 0.08–0.12. From a visual perspective, this algorithm successfully complements mural images with complex textures and large missing areas; thus, it may contribute to digital restorations of ancient murals.

Highlights

Ancient murals are important cultural treasures that record information about religious and cultural characteristics, people’s living conditions and major events in various historical periods, which constitute important references for the study of ancient history
Experimental environment To verify the effectiveness of the proposed consistencyenhanced generative adversarial network (GAN), tests on mural image restoration were conducted
The software was written in Python 3.7, and TensorFlow was used as the framework for complete mural image restoration

Summary

Introduction

Ancient murals are important cultural treasures that record information about religious and cultural characteristics, people’s living conditions and major events in various historical periods, which constitute important references for the study of ancient history. Due to the primitive drawing techniques and the fragile materials used in their creation, these murals have undergone constant change throughout their long history. Digital restoration of ancient murals has made some progress in recent years. Based on two cases by Mario Sironi and Edmondo Bacci in Venice, Izzo et al [2] conducted a thorough study of materials and Italian mural painting techniques of different ages to understand their protection needs and formulate sustainable conservation plans. Abdel-Haliem et al [4] isolated and identified Streptomyces as the primary cause of the discoloration of tomb murals in ancient Egypt, which provided a new idea for Streptomyces elimination. Regarding the reinforcement of murals in the Mogao Grottoes as well as the validity of

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