Li Shuhui, Saunders Charles, Lum Daniel J, Murray-Bruce John, Goyal Vivek K, Čižmár Tomáš, Phillips David B
School of Physics and Astronomy, University of Exeter, Exeter, EX4 4QL, UK.
Wuhan National Laboratory for Optoelectronics, School of Optical and Electronic Information, Huazhong University of Science and Technology, Wuhan, 430074, Hubei, China.
Light Sci Appl. 2021 Apr 21;10(1):88. doi: 10.1038/s41377-021-00514-9.
The measurement of the optical transmission matrix (TM) of an opaque material is an advanced form of space-variant aberration correction. Beyond imaging, TM-based methods are emerging in a range of fields, including optical communications, micro-manipulation, and computing. In many cases, the TM is very sensitive to perturbations in the configuration of the scattering medium it represents. Therefore, applications often require an up-to-the-minute characterisation of the fragile TM, typically entailing hundreds to thousands of probe measurements. Here, we explore how these measurement requirements can be relaxed using the framework of compressive sensing, in which the incorporation of prior information enables accurate estimation from fewer measurements than the dimensionality of the TM we aim to reconstruct. Examples of such priors include knowledge of a memory effect linking the input and output fields, an approximate model of the optical system, or a recent but degraded TM measurement. We demonstrate this concept by reconstructing the full-size TM of a multimode fibre supporting 754 modes at compression ratios down to ∼5% with good fidelity. We show that in this case, imaging is still possible using TMs reconstructed at compression ratios down to ∼1% (eight probe measurements). This compressive TM sampling strategy is quite general and may be applied to a variety of other scattering samples, including diffusers, thin layers of tissue, fibre optics of any refractive profile, and reflections from opaque walls. These approaches offer a route towards the measurement of high-dimensional TMs either quickly or with access to limited numbers of measurements.
不透明材料光学传输矩阵(TM)的测量是一种先进的空间可变像差校正形式。除了成像领域,基于TM的方法正在一系列领域中兴起,包括光通信、微操纵和计算。在许多情况下,TM对其所代表的散射介质配置中的扰动非常敏感。因此,应用通常需要对脆弱的TM进行实时表征,这通常需要进行数百到数千次探测测量。在这里,我们探讨如何使用压缩感知框架来放宽这些测量要求,在该框架中,纳入先验信息能够从比我们旨在重建的TM维度更少的测量中进行准确估计。此类先验的示例包括将输入和输出场联系起来的记忆效应的知识、光学系统的近似模型或最近但已退化的TM测量。我们通过以低至约5%的压缩率重建支持754个模式的多模光纤的全尺寸TM并具有良好的保真度来证明这一概念。我们表明,在这种情况下,使用压缩率低至约1%(八次探测测量)重建的TM仍然可以成像。这种压缩TM采样策略非常通用,可应用于各种其他散射样本,包括扩散器、薄组织层、任何折射率分布的光纤以及来自不透明壁的反射。这些方法为快速测量高维TM或在测量数量有限的情况下进行测量提供了一条途径。