LAMA通过双域学习重建，提升稀疏视角CT的精度与稳定性。
LAMA: Stable Dual-Domain Deep Reconstruction For Sparse-View CT
- 结合数据驱动与经典方法，用可学习正则化实现双域优化。
- 在多个基准数据集上优于现有最优方法，提升重建精度与稳定性。
- 适合需要高精度、低计算开销的医学影像重建任务。
反问题广泛存在于各类应用中，尤其在断层成像领域。我们提出一种学习型交替最小化算法（LAMA），通过两块优化融合数据驱动与经典技术，并具备收敛性保障。LAMA源自一个变分模型，其在数据域和图像域均采用神经网络参数化的可学习正则项，利用特定领域数据训练。允许正则项非凸且非光滑，以更有效提取数据特征。通过Nesterov平滑技术和残差学习结构最小化整体目标函数。实验表明，LAMA降低了网络复杂度，提升了内存效率，增强了重建精度、稳定性与可解释性。大量实验证明，该方法在常用计算机断层扫描基准数据集上显著优于当前最优方法。
原文摘要 · Abstract (English)
Inverse problems arise in many applications, especially tomographic imaging. We develop a Learned Alternating Minimization Algorithm (LAMA) to solve such problems via two-block optimization by synergizing data-driven and classical techniques with proven convergence. LAMA is naturally induced by a variational model with learnable regularizers in both data and image domains, parameterized as composite functions of neural networks trained with domain-specific data. We allow these regularizers to be nonconvex and nonsmooth to extract features from data effectively. We minimize the overall objective function using Nesterov's smoothing technique and residual learning architecture. It is demonstrated that LAMA reduces network complexity, improves memory efficiency, and enhances reconstruction accuracy, stability, and interpretability. Extensive experiments show that LAMA significantly outperforms state-of-the-art methods on popular benchmark datasets for Computed Tomography.
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