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Implicit information exploration techniques are of great importance for the restoration and conservation of cultural relics. At present, the hyperspectral image analysis technique is one of the main methods to extract hidden information, which mainly contains two analysis methods such as principal component analysis (PCA) and minimum noise fraction rotation (MNF), both of which have achieved certain information extraction effects. In recent years, with the development of artificial intelligence, deep learning, and other technologies, nonlinear methods such as neural networks are expected to further improve the effect of implicit information mining. Therefore, this paper is oriented to the problem of extracting hidden information from pottery artifacts and tries to study and explore the hidden information mining method based on deep neural networks, expecting to obtain more stable and richer hidden information. In this paper, an auto-encoder-based implied information mining method is proposed first, and the auto-encoder (AE) framework achieves good performance in feature learning by automatically learning low-dimensional embedding and reconstructing data. However, during the experiments, it is found that some important detailed information (e.g., implicit information) is often lost in the reconstruction process because the traditional autoencoder network only focuses more on the pixel-level reconstruction loss and ignores the overall distribution. Therefore, this paper further proposes a multi-scale convolutional autoencoder network (MSCAE). It constructs a multi-scale convolutional module based on the traditional AE and designs a cyclic consistency loss in addition to the reconstruction loss, to reduce the loss of detailed information in the reconstruction process and improve the implicit information mining effect. In the experiments, we find that the proposed method can achieve effective implied information mining by extracting implied information from cocoon-shaped pots, and its visual effect has been improved compared with the traditional AE network.
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