用神经网络+优化模型，让纳米断层成像更清晰锐利
High-Quality Tomographic Image Reconstruction Integrating Neural Networks and Mathematical Optimization
- 用神经网络识别局部边缘，再嵌入优化模型约束重建
- 实验数据上接口锐度和材料均匀性显著优于基准算法
- 适合需要高精度界面细节的材料科学成像研究
本文提出一种新型图像重建技术，用于基于投影的纳米与微断层成像。针对由均质材料相通过锐边连接的样品，通过训练神经网络识别子图像中的边缘特征，并将其集成到数学优化模型中，以减少以往重建中的伪影。该优化方法依据学习到的边缘预测偏好解，但若原始数据强烈支持其他解，则仍可选择替代方案。因此，该方法有效融合了样品均质性与锐边存在的先验知识，成功消除模糊现象。在实验数据集上的结果表明，相比基准算法，本方法在界面锐度和材料均匀性方面均有显著提升，能够生成高质量重建图像，展现出推动断层成像技术发展的潜力。
原文摘要 · Abstract (English)
In this work, we develop a novel technique for reconstructing images from projection-based nano- and microtomography. Our contribution focuses on enhancing reconstruction quality, particularly for specimen composed of homogeneous material phases connected by sharp edges. This is accomplished by training a neural network to identify edges within subpictures. The trained network is then integrated into a mathematical optimization model, to reduce artifacts from previous reconstructions. To this end, the optimization approach favors solutions according to the learned predictions, however may also determine alternative solutions if these are strongly supported by the raw data. Hence, our technique successfully incorporates knowledge about the homogeneity and presence of sharp edges in the sample and thereby eliminates blurriness. Our results on experimental datasets show significant enhancements in interface sharpness and material homogeneity compared to benchmark algorithms. Thus, our technique produces high-quality reconstructions, showcasing its potential for advancing tomographic imaging techniques.
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