Extraction method of Yuan blue and white porcelain pattern based on multi-scale Retinex and histogram multi-peak threshold segmentation

IF 2.6 1区 艺术学 Q2 CHEMISTRY, ANALYTICAL
Qi Zheng, Baoxi Zhu, Qin Cai, Jiao Li, Changfu Fang, Nanxing Wu
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引用次数: 0

Abstract

Aiming at the problem of "crystallization" on the surface of Yuan blue and white ceramics, which causes reflections and loss of image texture, an image processing method is proposed to repair the image texture information. A multi-scale Retinex pre-processing method is proposed to enhance the contrast between the pattern and the background. A color factor is introduced to prevent color distortion. A weighted average function is constructed to enhance image details and improve texture information. The Yuan blue and white pattern can be effectively segmented from the background using a combination of multi-peak thresholding for segmentation and other techniques. The experimental results demonstrate that, in comparison to other algorithms, the multi-scale Retinex and histogram multi-peak threshold coupled segmentation method proposed in this paper exhibits the highest F1-score of 0.03067 and an accuracy of 92.67% in cross-evaluation with other algorithms. This indicates that the overall performance of the algorithm is the best. The proposed method has the potential to inform the protection of cultural relics.

Abstract Image

基于多尺度 Retinex 和直方图多峰阈值分割的元青花瓷器图案提取方法
针对元青花陶瓷表面 "结晶 "导致反光和图像纹理丢失的问题,提出了一种修复图像纹理信息的图像处理方法。提出了一种多尺度 Retinex 预处理方法,以增强图案与背景之间的对比度。引入色彩因子以防止色彩失真。构建加权平均函数来增强图像细节,改善纹理信息。结合使用多峰阈值分割和其他技术,可以有效地将元蓝白图案从背景中分割出来。实验结果表明,与其他算法相比,本文提出的多尺度 Retinex 和直方图多峰阈值耦合分割方法的 F1 分数最高,为 0.03067,与其他算法交叉评价的准确率为 92.67%。这表明该算法的整体性能是最好的。本文提出的方法有望为文物保护提供参考。
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来源期刊
Heritage Science
Heritage Science Arts and Humanities-Conservation
CiteScore
4.00
自引率
20.00%
发文量
183
审稿时长
19 weeks
期刊介绍: Heritage Science is an open access journal publishing original peer-reviewed research covering: Understanding of the manufacturing processes, provenances, and environmental contexts of material types, objects, and buildings, of cultural significance including their historical significance. Understanding and prediction of physico-chemical and biological degradation processes of cultural artefacts, including climate change, and predictive heritage studies. Development and application of analytical and imaging methods or equipments for non-invasive, non-destructive or portable analysis of artwork and objects of cultural significance to identify component materials, degradation products and deterioration markers. Development and application of invasive and destructive methods for understanding the provenance of objects of cultural significance. Development and critical assessment of treatment materials and methods for artwork and objects of cultural significance. Development and application of statistical methods and algorithms for data analysis to further understanding of culturally significant objects. Publication of reference and corpus datasets as supplementary information to the statistical and analytical studies above. Description of novel technologies that can assist in the understanding of cultural heritage.
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