Restoration of an ancient temple mural by a local search algorithm of an adaptive sample block

Jianfang Cao,Hongyan Cui,Qi Zhang,Yanfei Li

doi:10.1186/s40494-019-0281-y

Jianfang Cao, Hongyan Cui + Show 2 more

Open Access

https://doi.org/10.1186/s40494-019-0281-y

Copy DOI

Abstract

Long-term influences in the external environment, including light, temperature and humidity, have caused varying degrees of damage to ancient Chinese murals. Allowing people to appreciate the original style of the murals has become important to experts, and the development of image processing and machine learning technology has allowed intelligent restoration of ancient murals. This paper proposed the adaptive sample block and local search (ASB–LS) algorithm based on the Criminisi algorithm to address the flaking deterioration of Kaihua Temple murals from the Song Dynasty. ASB–LS achieved virtual restoration of damaged areas. First, the mural’s compositional characteristics were analyzed, the structure tensor was introduced, and the data items were redefined using eigenvalues to ensure accurate transmission of the image’s structural information. Then, the data item was used to form a new priority function to improve the image filling order. Finally, the sample block size was adaptively selected by the average correlation of the structure tensor, and a local search strategy was used to improve matching efficiency, which effectively avoided mispropagation of the restored image structure and blinded search of the matching block. Experiments were performed on the Song Dynasty murals in the Kaihua Temple for two types of deterioration: flaking deterioration and artificial destruction. Compared with the Criminisi algorithm and two improved algorithms, the proposed ASB–LS algorithm had better subjective analysis and objective evaluation. Subjective visuals significantly improved and conformed to the image’s compositional characteristics, and the inpainting time efficiency improved, establishing a good foundation for restoring ancient murals.

Highlights

Ancient murals record large amounts of important information about people’s production, life and hobbies at that time
The Criminisi algorithm does not take the composition features of murals into consideration during inpainting. Considering these drawbacks of the Criminisi algorithm, this study introduced the structural tensor of the image, redefined the data item and priority function, and selected different block sizes adaptively for different structural regions of the mural to perform local searches for the optimum matched block
Adaptive sample block In the traditional Criminisi algorithm, when searching for the optimal matching block, the sample block is usually set to a fixed size, that is, a 9 × 9 pixel block, which results in structural disorder and discontinuity after the complicated image is repaired

Summary

Introduction

Ancient murals record large amounts of important information about people’s production, life and hobbies at that time. Yang et al [6, 7] applied the improved Criminisi algorithm, Markov sampling and image decomposition techniques to the digital restoration of the Dunhuang murals and achieved good repair results. Liu et al [16] modified the confidence term into an exponential form, which can reduce its tendency to rapidly fall to zero and can add a positive planning factor that the user can choose These two improved algorithms have a better effect on the repair of damaged images with a simple structure, but for images with rich texture, the structure is discontinuous. The improved algorithm described above can still cause structural discontinuity when inpainting complex mural images The reason for this analysis is mainly the priority function and the use of fixed sample blocks when inpainting different structural regions. A local search strategy was used to improve the search mode of the matching block, which improved the repair effect and repair efficiency of temple murals

Methods

Priority calculation of the boundary points

Results and discussion