论文标题
深度平衡体系结构的鲁棒性与测量模型的变化变化
Robustness of Deep Equilibrium Architectures to Changes in the Measurement Model
论文作者
论文摘要
基于模型的深层体系结构（DMBA）广泛用于成像逆问题中，以整合物理测量模型和学习的图像先验。插入式先验（PNP）和深平衡模型（DEQ）是两个DMBA框架，他们受到了极大的关注。两者之间的关键区别在于，使用特定的测量模型对DEQ中的图像进行了训练，而PNP中的图像是训练的。与DEQ相比，这种差异是普遍的假设，即PNP对测量模型的变化更为强大。本文研究了DEQ先验对测量模型变化的鲁棒性。我们在两个成像逆问题上的结果表明，在不匹配的测量模型下训练的DEQ先验优于图像Deoisers。
Deep model-based architectures (DMBAs) are widely used in imaging inverse problems to integrate physical measurement models and learned image priors. Plug-and-play priors (PnP) and deep equilibrium models (DEQ) are two DMBA frameworks that have received significant attention. The key difference between the two is that the image prior in DEQ is trained by using a specific measurement model, while that in PnP is trained as a general image denoiser. This difference is behind a common assumption that PnP is more robust to changes in the measurement models compared to DEQ. This paper investigates the robustness of DEQ priors to changes in the measurement models. Our results on two imaging inverse problems suggest that DEQ priors trained under mismatched measurement models outperform image denoisers.
