Mixed X-Ray Image Separation for Artworks With Concealed Designs.
Mixed X-Ray Image Separation for Artworks With Concealed Designs.
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具有隐藏设计的艺术品的混合 X 射线图像分离。
DOI：
10.1109/tip.2022.3185488
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发表时间：
2022
期刊：
影响因子：
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通讯作者：
Pu W
中科院分区：
文献类型：
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作者：
Pu W
在本文中，我们专注于X射线图像（X射线照片）的绘画与隐藏的子表面设计（例如，源自绘画支持的再利用或艺术家对作品的修改），因此包括表面绘画和隐藏特征的贡献。特别是，我们提出了一种基于自监督深度学习的图像分离方法，可以应用于这些绘画的X射线图像，将它们分离为两个假设的X射线图像。这些重建图像之一与隐藏绘画的X射线图像相关，而第二个仅包含与可见绘画的X射线图像相关的信息。所提出的分离网络由两部分组成：分析和合成子网络。分析子网络基于使用算法展开技术设计的学习耦合迭代收缩阈值算法（LCISTA），合成子网络由多个线性映射组成。学习算法以完全自我监督的方式操作，而不需要包含混合X射线图像和分离的X射线图像的样本集。所提出的方法在一幅具有隐藏内容的真实的画作上进行了演示，该画作是弗朗西斯科·德·戈亚的《唐娜·伊莎贝尔·德·波塞尔》，以证明其有效性。
In this paper, we focus on X-ray images (X-radiographs) of paintings with concealed sub-surface designs (e.g., deriving from reuse of the painting support or revision of a composition by the artist), which therefore include contributions from both the surface painting and the concealed features. In particular, we propose a self-supervised deep learning-based image separation approach that can be applied to the X-ray images from such paintings to separate them into two hypothetical X-ray images. One of these reconstructed images is related to the X-ray image of the concealed painting, while the second one contains only information related to the X-ray image of the visible painting. The proposed separation network consists of two components: the analysis and the synthesis sub-networks. The analysis sub-network is based on learned coupled iterative shrinkage thresholding algorithms (LCISTA) designed using algorithm unrolling techniques, and the synthesis sub-network consists of several linear mappings. The learning algorithm operates in a totally self-supervised fashion without requiring a sample set that contains both the mixed X-ray images and the separated ones. The proposed method is demonstrated on a real painting with concealed content, Do na Isabel de Porcel by Francisco de Goya, to show its effectiveness.