Solutions to inverse problems with dictionary learning and transform learning are well known. In recent years, their graph regularized versions have also been proposed. Graph regularization introduces non-local smoothness to spatially diverse but structurally similar patches. A new approach to solve inverse problems, based on the autoencoder has been introduced lately. In this work, we propose graph regularization on autoencoder and show how it can be used for solving inverse problems. We evaluate different approaches to MRI reconstruction. Results show that our method improves over existing generic representation learning based inversion techniques and several state-of-the-art techniques that are tailored for this particular problem.

字典学习和变换学习的逆问题的解决方案是众所周知的。近年来，还提出了他们的图正则化版本。图正则化为空间多样但结构相似的补丁引入了非局部平滑性。最近引入了一种基于自编码器解决逆问题的新方法。在这项工作中，我们提出了自动编码器上的图正则化，并展示了它如何用于解决逆问题。我们评估了不同的 MRI 重建方法。结果表明，我们的方法改进了现有的基于通用表示学习的反演技术和为这个特定问题量身定制的几种最先进的技术。