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.

翻译：X射线层析成像中的图像重建是一个不适定逆问题，尤其在可用数据有限的情况下。因此正则化至关重要，但其有效性取决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。本文提出一种基于同一问题的两种不同计算离散化模型的自动参数选择新方法。通过反馈控制算法动态调整正则化强度，驱动迭代重建过程趋向于使两个网格上的重建结果达到充分相似的最小参数值。使用真实层析数据验证了所提方法的有效性。