用深度自编码器压缩工业X射线断层图像，兼顾存储与细节保留。
D-CNN and VQ-VAE Autoencoders for Compression and Denoising of Industrial X-ray Computed Tomography Images
- 对比D-CNN与VQ-VAE两种网络压缩工业XCT数据。
- 不同压缩率下，图像重建质量差异显著，需依分析需求选择。
- 新增边缘保持指标，更适配三维孔隙结构分析。
成像技术进步导致影像科学数据量持续增长，亟需高效可靠的存储方案。本研究利用深度学习自编码器对工业X射线计算机断层扫描（XCT）数据进行压缩，并评估压缩算法对重建数据质量的影响。采用两种不同压缩率的网络架构：深层卷积神经网络（D-CNN）和向量量化变分自编码器（VQ-VAE）。实验使用具有复杂内部孔隙网络的砂岩样本的XCT数据，量化并比较了两种架构在不同压缩率下的解码图像质量。此外，为提升重建图像质量评估，引入一种对边缘保持敏感的新指标，这对三维数据分析至关重要。结果表明，不同架构和压缩率需根据后续分析所需保留的特征而定。研究成果可帮助科研人员制定数据存储与分析策略。
原文摘要 · Abstract (English)
The ever-growing volume of data in imaging sciences stemming from the advancements in imaging technologies, necessitates efficient and reliable storage solutions for such large datasets. This study investigates the compression of industrial X-ray computed tomography (XCT) data using deep learning autoencoders and examines how these compression algorithms affect the quality of the recovered data. Two network architectures with different compression rates were used, a deep convolution neural network (D-CNN) and a vector quantized variational autoencoder (VQ-VAE). The XCT data used was from a sandstone sample with a complex internal pore network. The quality of the decoded images obtained from the two different deep learning architectures with different compression rates were quantified and compared to the original input data. In addition, to improve image decoding quality metrics, we introduced a metric sensitive to edge preservation, which is crucial for three-dimensional data analysis. We showed that different architectures and compression rates are required depending on the specific characteristics needed to be preserved for later analysis. The findings presented here can aid scientists to determine the requirements and strategies for their data storage and analysis needs.
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