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Application of Digital Processing in Relic Image Restoration Design

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Abstract

Cultural relic is the carrier of human historic culture, which can reflect the cultural and social environment, but cultural relics as a material will be damaged over time. Before the advent of computer technology, the damaged cultural relics would not be repaired due to cost. Computer vision technology has been applied to the restoration of cultural relics, mainly for the virtual restoration of damaged cultural relics images. This paper briefly introduced the Criminisi image restoration algorithm and the structure tensor used to improve the algorithm in the digital cultural relics image restoration. A damaged cultural relics image and a complete image which was damaged by human were repaired respectively using the classical Criminisi image restoration algorithm and the improved structure tensor based repair algorithm on MATLAB software. The results showed that the Criminisi image restoration algorithm could be used to repair the damaged images of ancient fabrics. It was found that the classical image restoration algorithm had some shortcomings, such as inappropriate texture structure, obvious repair marks and addition of redundant information, but the improved algorithm effectively avoided the above shortcomings. The peak signal to noise ratio (SNR) of the complete image which was damaged by human was compared objectively, and it was found that the improved algorithm had better restoration performance.

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Authors and Affiliations

  1. School of Information and Technology, Northwest University, Xi’an, 710127, Shaanxi, China

    Guohua Geng

  2. Xi’an University of Finance and Economics, Xi’an, 710100, Shaanxi, China

    Hui Tang

  3. College of Information Science and Technology, Beijing Normal University, Beijing, 100875, China

    Mingquan zhou

Authors
  1. Hui Tang
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  2. Guohua Geng
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  3. Mingquan zhou
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Correspondence to Hui Tang.

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Tang, H., Geng, G. & zhou, M. Application of Digital Processing in Relic Image Restoration Design. Sens Imaging 21, 6 (2020). https://doi.org/10.1007/s11220-019-0265-8

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  • DOI: https://doi.org/10.1007/s11220-019-0265-8

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