论文标题
通过基于深度学习的先验进行展开的优化
Unrolled Optimization with Deep Learning-based Priors for Phaseless Inverse Scattering Problems
论文作者
论文摘要
反向散射问题，例如使用无相度数据（PD-ISP）进行电磁成像中的问题，涉及使用波散射的无音测量对象进行成像。在极强的散射条件下，例如物体具有很高的介电常数或大小较大的情况，这种反问题可能是高度非线性和不适的。在这项工作中，我们提出了一个端到端的重建框架，并使用深度先验的展开优化在非常强大的散射条件下解决PD-ISP。我们将基于线性物理的近似模型纳入了我们的优化框架，以及基于深度学习的先验，并使用迭代算法解决了所得问题，该算法被展开到深网络中。该网络不仅学习了数据驱动的正则化，而且还克服了近似线性模型的缺点，并学习了非线性功能。更重要的是，与现有的PD-ISP方法不同，所提出的框架将所有可调参数的最佳值（包括多个正则化参数）作为框架的一部分。使用2.4 GHz无用的Wi-Fi测量结果显示了用于室内成像的使用情况的模拟和实验的结果，其中这些物体表现出极强的散射和低吸收。结果表明，所提出的框架的表现优于现有的模型驱动和数据驱动的技术，而有效性范围高达20倍。
Inverse scattering problems, such as those in electromagnetic imaging using phaseless data (PD-ISPs), involve imaging objects using phaseless measurements of wave scattering. Such inverse problems can be highly non-linear and ill-posed under extremely strong scattering conditions such as when the objects have very high permittivity or are large in size. In this work, we propose an end-to-end reconstruction framework using unrolled optimization with deep priors to solve PD-ISPs under very strong scattering conditions. We incorporate an approximate linear physics-based model into our optimization framework along with a deep learning-based prior and solve the resulting problem using an iterative algorithm which is unfolded into a deep network. This network not only learns data-driven regularization, but also overcomes the shortcomings of approximate linear models and learns non-linear features. More important, unlike existing PD-ISP methods, the proposed framework learns optimum values of all tunable parameters (including multiple regularization parameters) as a part of the framework. Results from simulations and experiments are shown for the use case of indoor imaging using 2.4 GHz phaseless Wi-Fi measurements, where the objects exhibit extremely strong scattering and low-absorption. Results show that the proposed framework outperforms existing model-driven and data-driven techniques by a significant margin and provides up to 20 times higher validity range.
