用可重构超表面实现短波红外压缩感知光谱成像
Inverse Design of Metasurface for Spectral Imaging
- 物理与数据协同建模，端到端优化超表面结构与重建网络
- 重建信噪比提升最高7.6分贝，抗噪性与测量矩阵性能改善
- 适合做高精度红外光谱成像的紧凑系统研发人员
在计算光学中，联合优化光学调制与算法解码的超表面逆向设计面临重大挑战，尤其在高光谱成像应用中。本文提出一种物理-数据协同驱动框架，利用相变材料Ge2Sb2Se4Te1制备可重构超表面，实现短波红外区域的紧凑型压缩感知光谱成像。核心是基于32万余组仿真几何结构训练的可微神经模拟器，能准确预测11种晶化状态下的光谱响应。该可微性支持超表面结构、光谱编码函数与深度重建网络的端到端联合优化。我们还提出软形状正则化技术，在梯度更新中保持可制造性。实验表明，优化系统在峰值信噪比上最高提升7.6 dB，具备更强抗噪能力与更优测量矩阵条件，验证了该方法在高性能高光谱成像中的潜力。
原文摘要 · Abstract (English)
Inverse design of metasurfaces for the joint optimization of optical modulation and algorithmic decoding in computational optics presents significant challenges, especially in applications such as hyperspectral imaging. We introduce a physics-data co-driven framework for designing reconfigurable metasurfaces fabricated from the phase-change material Ge2Sb2Se4Te1 to achieve compact, compressive spectral imaging in the shortwave infrared region. Central to our approach is a differentiable neural simulator, trained on over 320,000 simulated geometries, that accurately predicts spectral responses across 11 crystallization states. This differentiability enables end-to-end joint optimization of the metasurface geometry, its spectral encoding function, and a deep reconstruction network. We also propose a soft shape regularization technique that preserves manufacturability during gradient-based updates. Experiments show that our optimized system improves reconstruction fidelity by up to 7.6 dB in the peak-signal-to-noise ratio, with enhanced noise resilience and improved measurement matrix conditioning, underscoring the potential of our approach for high-performance hyperspectral imaging.
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