自动选择断层成像正则化参数，无需人工干预。
Automatic regularization parameter choice for tomography using a double model approach
- 用两个不同网格的模型互相校验，动态调节正则化强度。
- 在真实断层数据上验证，可稳定找到合适参数。
- 适合希望省去调参步骤的工业或科研用户。
X射线断层成像中的图像重建是一个病态逆问题，尤其在数据有限时更为显著。正则化对于获得可靠结果至关重要，但其效果高度依赖于正则化参数的选择，该参数需在数据保真度与先验信息之间取得平衡。本文提出一种新型自动参数选择方法，基于同一问题的两种不同计算离散化方案。通过反馈控制算法动态调整正则化强度，使迭代重建过程收敛到两个网格上重建结果足够相似的最小参数值。该方法在真实断层数据上进行了验证，结果表明其能有效实现参数自适应选择。
原文摘要 · Abstract (English)
Image reconstruction in X-ray tomography is an ill-posed inverse problem, particularly with limited available data. Regularization is thus essential, but its effectiveness hinges on the choice of a regularization parameter that balances data fidelity against a priori information. We present a novel method for automatic parameter selection based on the use of two distinct computational discretizations of the same problem. A feedback control algorithm dynamically adjusts the regularization strength, driving an iterative reconstruction toward the smallest parameter that yields sufficient similarity between reconstructions on the two grids. The effectiveness of the proposed approach is demonstrated using real tomographic data.
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