Scratch detection of archival films: modeling and trajectory calculation

IF 2.6 1区艺术学 Q2 CHEMISTRY, ANALYTICAL

Heritage Science Pub Date : 2024-01-02 DOI:10.1186/s40494-023-01119-8

Quanyang Liu, Yunqing Liu, Fei Yan

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引用次数: 0

Abstract

Scratches are a common continuous structural type of damage in archival films. Scratch pixel level segmentation algorithms still use general image segmentation, and with poor the detection. Based on the characteristics of scratches, a scratch model based on a Gaussian probability density function is proposed, a local adaptive one-dimensional Gaussian weighted segmentation algorithm is designed, and an invalid region filtering mechanism is described. Based on experimental results, the local one-dimensional Gaussian weighted segmentation algorithm proposed in this paper can preserve the integrity and continuity of scratches, and effectively suppresses edge defects; the invalid region filtering mechanism can effectively reduce the false detection of scratches. Adding a scratch trajectory can effectively reduce the chances of missing the detection of scratches, and the stability of the traditional algorithm using Kalman filters to predict the scratch trajectory is poor. According to the similarity of multiple scratch trajectories, a calculation method of trajectories based on U-net is described. Experimental results show that this method can calculate the scratch trajectory stably and effectively.

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档案胶片划痕检测：建模和轨迹计算

划痕是档案胶片中常见的一种连续结构性损伤。划痕像素级分割算法仍采用一般的图像分割，检测效果较差。根据划痕的特点，提出了基于高斯概率密度函数的划痕模型，设计了局部自适应一维高斯加权分割算法，并描述了无效区域过滤机制。根据实验结果，本文提出的局部一维高斯加权分割算法能保持划痕的完整性和连续性，有效抑制边缘缺陷；无效区域过滤机制能有效降低划痕的误检率。添加划痕轨迹可有效降低划痕漏检几率，而传统算法使用卡尔曼滤波器预测划痕轨迹的稳定性较差。根据多个划痕轨迹的相似性，介绍了一种基于 U 网的轨迹计算方法。实验结果表明，该方法能稳定有效地计算划痕轨迹。

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来源期刊

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.