Su Linglong, Ma Lihong, Wang Hui
Appl Opt. 2015 Feb 1;54(4):859-68. doi: 10.1364/AO.54.000859.
In this paper, we propose an improved deterministic regularization algorithm to handle the sparse angle data problem in optical diffraction tomography. Based on optical diffraction tomography and the deterministic regularization algorithm, the regularization iteration is performed in the space domain and the frequency domain simultaneously, which greatly reduces the computational cost. By applying piecewise-smoothness and positivity constraints as the penalty function, the missing frequency spectrum is effectively recovered and the internal refractive index distribution of the specimen is accurately reconstructed. Using simulated and experimental results, we show that the proposed regularization algorithm allows accurate refractive index reconstruction from very sparse angle data in optical diffraction tomography.
在本文中，我们提出一种改进的确定性正则化算法，以处理光学衍射层析成像中的稀疏角度数据问题。基于光学衍射层析成像和确定性正则化算法，正则化迭代在空间域和频率域同时进行，这大大降低了计算成本。通过应用分段光滑性和正性约束作为惩罚函数，有效地恢复了缺失的频谱，并准确地重建了样本的内部折射率分布。利用模拟和实验结果，我们表明所提出的正则化算法能够从光学衍射层析成像中非常稀疏的角度数据准确重建折射率。
