面向压缩感知成像的超表面端到端优化
End-to-End Optimization of Metasurfaces for Imaging with Compressed Sensing
AI总结：
提出一种端到端优化超表面成像系统的框架，通过嵌套无近似的压缩感知重建算法，自动发现最优超表面结构，显著提升对噪声的鲁棒性并逼近压缩感知的数学极限。
摘要：
我们提出了一个用于端到端优化超表面成像系统的框架，该系统利用压缩感知重建目标。压缩感知是一种在目标物体表现出稀疏性（即物体可由少量非零值描述，但这些值的位置未知）时求解欠定成像问题的技术。我们将一种迭代的、无近似的压缩感知重建算法嵌套到端到端优化流程中，从而得到一种可解释且数据高效的方法，以最大化利用超光学来利用物体稀疏性。我们将该框架应用于采用相变材料的超分辨率成像和高分辨率深度成像。在这两种情况下，我们的端到端框架通过计算发现了用于压缩感知恢复的最优超表面结构，自动平衡了众多复杂的设计考量，从而从具有数百万维度的复杂物理约束流形中选择成像测量矩阵。优化后的超表面成像系统对噪声具有鲁棒性，显著优于随机散射表面，并逼近高斯矩阵的理想压缩感知性能，展示了物理超表面系统如何可证明地逼近压缩感知的数学极限。
英文摘要：
We present a framework for the end-to-end optimization of metasurface imaging systems that reconstruct targets using compressed sensing, a technique for solving underdetermined imaging problems when the target object exhibits sparsity (i.e. the object can be described by a small number of non-zero values, but the positions of these values are unknown). We nest an iterative, unapproximated compressed sensing reconstruction algorithm into our end-to-end optimization pipeline, resulting in an interpretable, data-efficient method for maximally leveraging metaoptics to exploit object sparsity. We apply our framework to super-resolution imaging and high-resolution depth imaging with a phase-change material. In both situations, our end-to-end framework computationally discovers optimal metasurface structures for compressed sensing recovery, automatically balancing a number of complicated design considerations to select an imaging measurement matrix from a complex, physically constrained manifold with millions ofdimensions. The optimized metasurface imaging systems are robust to noise, significantly improving over random scattering surfaces and approaching the ideal compressed sensing performance of a Gaussian matrix, showing how a physical metasurface system can demonstrably approach the mathematical limits of compressed sensing.
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