The limited angle Radon transform is notoriously difficult to invert due to its ill-posedness. In this work, we give a mathematical explanation that data-driven approaches can stably reconstruct more information compared to traditional methods like filtered backprojection. In addition, we use experiments based on the U-Net neural network to validate our theory.

翻译：众所周知，由于不适定性，有限角度Radon变换的求逆极为困难。在本工作中，我们给出了一个数学解释，表明与传统方法（如滤波反投影）相比，数据驱动方法能够更稳定地重建更多信息。此外，我们基于U-Net神经网络进行了实验以验证我们的理论。