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Automatic identification and inpainting of cracks in mural images based on improved SOM

Country : Singapore
Department : Singapore Management University
Project Title : Automatic identification and inpainting of cracks in mural images based on improved SOM
Researcher : YANG, Ting , WANG, Shuangshuan , PEN, Haibo , WANG, Zhaoxia
Keyword : Automatic recognition , Image inpainting , Mural crack , Self-organizing map , Artificial Intelligence and Robotics , Operations Research, Systems Engineering and Industrial Engineering , Theory and Algorithms
Publisher : Institutional Knowledge at Singapore Management University
Year End : 2020
Identifier : https://ink.library.smu.edu.sg/sis_research/5496
Source : Research Collection School Of Information Systems
Abstract / Description :

In this paper, a self-organizing map(SOM)image restoration algorithm based on artificial neural network is proposed to repair in murals due to cracks. The algorithm is particularly applied to ancient architectural mural restoration. Considering the linear structural features of mural cracks, a multi-scale morphological edge gradient detection is applied to extract crack edge information, so that the gray level of the crack boundary region is made to change sharply, so as to achieve the effect of boundary accentuation. An adaptive threshold segmentation is performed on the transformed image to ensure that each pixel in the image belongs to the target region. The area involved is selected as the target area to remove the false target and achieve accurate extraction of damaged features, and automatic recognition and labeling of the damaged pixel is realized. The improved SOM algorithm is used to restore the damaged pixels by layering the image through SOM clustering, and the broken pixel values are iteratively calculated in a single layer to achieve accurate and efficient restoration of the layers of the image in parallel. Then, the layers are merged, and the marked parts are completely restored. Finally, the method is evaluated using the inpainting of three types of cracks in murals. The improved SOM algorithm proposed in this paper has significantly improved the inpainting ability according to four types of indicators, including the PSNR and FSIM indicators. Moreover, the average inpainting time is reduced by 40. 34%, which shows the effectiveness and superiority of the inpainting method for repairing cracks in ancient murals.

References

YANG, Ting and others / et al. (2020). Automatic identification and inpainting of cracks in mural images based on improved SOM.  Singapore: Singapore Management University.
YANG, Ting and others / et al. 2020. "Automatic identification and inpainting of cracks in mural images based on improved SOM".  Singapore: Singapore Management University.
YANG, Ting and others / et al. "Automatic identification and inpainting of cracks in mural images based on improved SOM."  Singapore: Singapore Management University, 2020. Print.
YANG, Ting and others / et al. Automatic identification and inpainting of cracks in mural images based on improved SOM. Singapore: Singapore Management University; 2020.

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