Automatic calibration of crack and flaking diseases in ancient temple murals

Abstract

Many precious ancient murals are seriously deteriorated due to long-term environmental influences and man-made destructions. How to effectively protect ancient murals and restore these murals’ original appearance has become an urgent problem for field experts. Modern computer technology makes it possible to virtually restore the deteriorated areas in ancient murals. However, most existing mural restoration approaches require manual calibration of the deteriorated areas, which is very difficult and time-consuming. It has been noticed that the earth layer flaking and cracks are the most common problems of ancient temple murals. This paper proposes an automatic calibration method for the earth layer flaking and cracking deterioration of murals by taking temple murals from the Ming Dynasty in Zhilin Temple as the study object. First, we extract the texture and line features of the deteriorated murals by using multi-dimensional gradient detection in the HSV space. Then, a guided filter operation is employed to highlight the disease (deteriorated) areas and meanwhile suppress other unwanted areas, which helps to extract the flaked areas or cracked lines from the digital murals. The filtered images are segmented by using an automatic threshold to obtain the initial masks of the mural disease areas. Next, we use a two-dimensional tensor voting technique to connect the discontinuous edge curves of the extracted disease areas. Afterwards, the masks of flaking and cracking areas can be generated after morphological processing. Finally, we obtained the calibration results by adding the masks to the original digital murals. Experimental results show that our method can rapidly and accurately calibrate the cracks and the earth layer flaking diseases in the ancient murals. As compared to existing calibration approaches, our method can achieve better performance in subjective visual quality and objective evaluation metrics. Moreover, the method does not need human-computer interaction. This research work provides a solid foundation for the following virtual and practical restoration of ancient murals.