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

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...

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Main Authors: YANG, Ting, WANG, Shuangshuan, PEN, Haibo, WANG, Zhaoxia
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Language:English
Published: Institutional Knowledge at Singapore Management University 2020
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Online Access:https://ink.library.smu.edu.sg/sis_research/5496
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spelling sg-smu-ink.sis_research-64992020-12-24T02:18:02Z Automatic identification and inpainting of cracks in mural images based on improved SOM YANG, Ting WANG, Shuangshuan PEN, Haibo WANG, Zhaoxia 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. 2020-09-15T07:00:00Z text https://ink.library.smu.edu.sg/sis_research/5496 info:doi/10.11784/tdxbz201907054 Research Collection School Of Computing and Information Systems eng Institutional Knowledge at Singapore Management University Automatic recognition Image inpainting Mural crack Self-organizing map Artificial Intelligence and Robotics Operations Research, Systems Engineering and Industrial Engineering Theory and Algorithms
institution Singapore Management University
building SMU Libraries
continent Asia
country Singapore
Singapore
content_provider SMU Libraries
collection InK@SMU
language English
topic Automatic recognition
Image inpainting
Mural crack
Self-organizing map
Artificial Intelligence and Robotics
Operations Research, Systems Engineering and Industrial Engineering
Theory and Algorithms
spellingShingle Automatic recognition
Image inpainting
Mural crack
Self-organizing map
Artificial Intelligence and Robotics
Operations Research, Systems Engineering and Industrial Engineering
Theory and Algorithms
YANG, Ting
WANG, Shuangshuan
PEN, Haibo
WANG, Zhaoxia
Automatic identification and inpainting of cracks in mural images based on improved SOM
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.
format text
author YANG, Ting
WANG, Shuangshuan
PEN, Haibo
WANG, Zhaoxia
author_facet YANG, Ting
WANG, Shuangshuan
PEN, Haibo
WANG, Zhaoxia
author_sort YANG, Ting
title Automatic identification and inpainting of cracks in mural images based on improved SOM
title_short Automatic identification and inpainting of cracks in mural images based on improved SOM
title_full Automatic identification and inpainting of cracks in mural images based on improved SOM
title_fullStr Automatic identification and inpainting of cracks in mural images based on improved SOM
title_full_unstemmed Automatic identification and inpainting of cracks in mural images based on improved SOM
title_sort automatic identification and inpainting of cracks in mural images based on improved som
publisher Institutional Knowledge at Singapore Management University
publishDate 2020
url https://ink.library.smu.edu.sg/sis_research/5496
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