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Extracting Painted Pottery Pattern Information Based on Deep Learning

机译:基于深度学习的绘画陶器模式信息

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This paper proposes a method that can effectively recover pattern information from painted pottery. The first step is to create an image of the pottery using hyperspectral imaging techniques. The Minimum Noise Fraction transform (MNF) is then used to reduce the dimensionality of the hyperspectral image to obtain the principal component image. Next, we propose a pattern extraction method based on deep learning, the topic of this paper, to further enhance the process resulting in more complete pattern information. Lastly, the pattern information image is fused with a true colour image using the improved sparse representation and detail injection fusion method to obtain an image that includes both the pattern and colour information of the painted pottery. The experimental results we observed confirm this process effectively extracts the pattern information from painted pottery.
机译:本文提出了一种能够有效地恢复彩绘陶器的模式信息的方法。第一步是使用高光谱成像技术创建陶器的图像。然后使用最小噪声分数变换(MNF)来降低高光谱图像的维度以获得主成分图像。接下来,我们提出了一种基于深度学习的模式提取方法,本文的主题,进一步增强过程,从而产生更完整的模式信息。最后,使用改进的稀疏表示和细节注射融合方法与真正的彩色图像融合,以获得包括涂漆陶器的图案和颜色信息的图像。我们观察到的实验结果证实了这一过程有效地提取了彩绘陶器的模式信息。

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