论文标题
通过深层隐式层来解决反问题的测量一致网络
Measurement-Consistent Networks via a Deep Implicit Layer for Solving Inverse Problems
论文作者
论文摘要
端到端的深度神经网络（DNN）已成为解决反问题的最先进（SOTA）。尽管表现出色，但在部署过程中，此类网络对测试管道中的微小变化很敏感，并且通常无法重建小但重要的细节，这是医学成像，天文学或防御的关键功能。 DNN中的这种不稳定性可以通过以下事实来解释：它们在部署过程中忽略了前向测量模型，因此无法在其输出和输入测量之间执行一致性。为了克服这一点，我们提出了一个框架，将任何DNN的反向问题转换为测量一致的框架。这是通过将其添加到旨在解决基于模型的优化问题的隐层（或深度平衡网络）上来完成的。隐式层由一个可学习的网络组成，可以将其集成到端到端训练中，同时保持SOTA DNN固定。单像超分辨率的实验表明，所提出的框架可显着改善SOTA DNN的重建质量和鲁棒性。
End-to-end deep neural networks (DNNs) have become the state-of-the-art (SOTA) for solving inverse problems. Despite their outstanding performance, during deployment, such networks are sensitive to minor variations in the testing pipeline and often fail to reconstruct small but important details, a feature critical in medical imaging, astronomy, or defence. Such instabilities in DNNs can be explained by the fact that they ignore the forward measurement model during deployment, and thus fail to enforce consistency between their output and the input measurements. To overcome this, we propose a framework that transforms any DNN for inverse problems into a measurement-consistent one. This is done by appending to it an implicit layer (or deep equilibrium network) designed to solve a model-based optimization problem. The implicit layer consists of a shallow learnable network that can be integrated into the end-to-end training while keeping the SOTA DNN fixed. Experiments on single-image super-resolution show that the proposed framework leads to significant improvements in reconstruction quality and robustness over the SOTA DNNs.
