用神经场建模磁化矢量场，抗噪能力强，精度更高。
Neural Vector Tomography for Reconstructing a Magnetization Vector Field
- 用平滑神经场替代离散像素建模矢量场
- 在噪声环境下仍能实现高质量重建
- 特别适合具有全局连续对称性的场景
离散化的矢量层析重建方法容易产生伪影，且噪声增加时性能进一步下降。本文改用平滑的神经场建模底层矢量场。由于神经网络激活函数可选为光滑函数，且空间域不再像素化，模型即使在噪声存在下也能生成高质量重建结果。当系统具有全局连续对称性时，该方法相比现有技术显著提升重建精度。
原文摘要 · Abstract (English)
Discretized techniques for vector tomographic reconstructions are prone to producing artifacts in the reconstructions. The quality of these reconstructions may further deteriorate as the amount of noise increases. In this work, we instead model the underlying vector fields using smooth neural fields. Owing to the fact that the activation functions in the neural network may be chosen to be smooth and the domain is no longer pixelated, the model results in high-quality reconstructions, even under presence of noise. In the case where we have underlying global continuous symmetry, we find that the neural network substantially improves the accuracy of the reconstruction over the existing techniques.
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