利用空心自组装衍射薄膜的偏振压缩感知
Polarimetric compressed sensing with hollow, self-assembled diffractive films
AI总结：
提出一种基于低折射率自组装空心纳米腔阵列光学编码器与神经网络结合的纳米光子学压缩感知方法，实现了波前方向和偏振的高分辨率预测，为偏振编码、光学计算与成像提供了新型材料方案。
摘要：
感知光的偏振和波前方向能够实现表面曲率评估、材料识别、阴影区分，并改善浑浊环境中的图像质量。传统偏振相机在每个像素上利用多次传感器测量和偏振滤波光学器件，这导致图像分辨率降低。我们提出了一种纳米光子学流水线，利用低折射率、自组装的光学编码器实现压缩感知并降低采样要求。这些纳米结构将光散射到晶格模式中，在衍射干涉图案的线偏振分量中编码波前方向和偏振椭圆率。将光学编码器与神经网络相结合，当干涉图案被充分采样时，系统可预测指向和偏振。对“有序”和“随机”光学编码器的比较表明，后者既能模糊干涉图案，又能实现更高的分辨率。我们的工作核心是自组装空心纳米腔阵列对入射光偏振的意外调制和空间复用，作为一类区别于传统超表面的材料，这不仅将实现偏振和光学计算的编码，还将实现压缩感知和成像。
英文摘要：
Sensing light's polarization and wavefront direction enables surface curvature assessment, material identification, shadow differentiation, and improved image quality in turbid environments. Traditional polarization cameras utilize multiple sensor measurements per pixel and polarization-filtering optics, which result in reduced image resolution. We propose a nanophotonic pipeline that enables compressive sensing and reduces the sampling requirements with a low-refractive-index, self-assembled optical encoder. These nanostructures scatter light into lattice modes, which encode the wavefront direction and the polarization ellipticity in the linearly-polarized components of the diffracted, interference patterns. Combining optical encoders with a neural network, the system predicts pointing and polarization when the interference patterns are adequately sampled. A comparison of ``ordered'' and ``random'' optical encoders shows that the latter both blurs the interference patterns and achieves higher resolution. Our work centers on the unexpected modulation and spatial multiplexing of incident light polarization by self-assembled hollow nanocavity arrays as a class of materials distinct from traditional metasurfaces that will not only enable encoding for polarization and optical computing but also for compressed sensing and imaging.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
