Pigment Clustering Inpainting on Residual Mural by Improved K-means
- Release time:2024-08-09
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Affiliation of Author(s):
信息与控制工程学院
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Journal:
Journal of Residuals science & Technology
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Key Words:
中文关键字：颜料聚类；图像修复；壁画，英文关键字：pigment clustering;inpainting;mural
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Abstract:
Ancient murals have survived in the past hundredsyears, there areparts of them left
with many disease. These murals carry the history information and need to be restored. Image
inpainting as an effective restoration technology is usually used to rebuild the damaged
information. Ming temple mural is colorful for plenty of pigments, so it is need to find exemplars
from different pigment regions. These murals use Chinese traditional painting principle named five
elementsinclude earth, water, metal, fire, and wood. So we design a novel mural inpainting system
using improved k-means to cluster the mural pigments and optimize the inpainting algorithm to
find the proper exemplars. Firstly we choose random k to get the preliminary results, it just take
the Euclidean distance without image color feature. So we add Bhattacharyya coefficients to reject
the similar clustering; secondly we transfer the image from RGB to Lab space which have more
color expressions for Ming temple mural's pigments and the colors have more relationships with
each other; thirdly we inpaint the mural's missing portions in Lab space and change the similarity
between exemplars by Bhattacharyya distance to get a better result. The improved k-means
clustering system save the running time, and the Lab space inpainting with novel distance
calculation insure the filling effects. The experimental results show that the improved technique
restores the Ming temple mural better and reduce the time consuming.
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Note:
吴萌
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Co-author:
王展
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First Author:
wanghuiqin,wumeng
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Indexed by:
Journal paper
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Volume:
卷：13
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Issue:
期：9
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Page Number:
页：24.1-24.8
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Translation or Not:
no
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Date of Publication:
2017-12-01
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