In this paper, we revisit a supervised learning approach based on unrolling, known as $\Psi$DONet, by providing a deeper microlocal interpretation for its theoretical analysis, and extending its study to the case of sparse-angle tomography. Furthermore, we refine the implementation of the original $\Psi$DONet considering special filters whose structure is specifically inspired by the streak artifact singularities characterizing tomographic reconstructions from incomplete data. This allows to considerably lower the number of (learnable) parameters while preserving (or even slightly improving) the same quality for the reconstructions from limited-angle data and providing a proof-of-concept for the case of sparse-angle tomographic data.

翻译：本文通过为其理论分析提供更深入的微局部解释，并拓展其至稀疏角度断层成像的研究，重新审视了一种基于展开的监督学习方法——ΨDONet。此外，我们改进了原始ΨDONet的实现，考虑了特殊滤波器的设计，其结构专门受到不完整数据断层重建中条纹伪影奇异性的启发。这能够在保持（甚至略微提升）有限角度数据重建质量的同时，显著减少（可学习）参数的数量，并为稀疏角度断层成像数据提供了概念验证。