Multi-Layered Surface Estimation for Low-Cost Optical Coherence Tomography

Abstract

Optical coherence tomography (OCT) has broad applicability for 3D sensing, such as reconstructing the surface profiles of multi-layered samples in industrial settings. However, accurately determining the number of layers and their precise locations is a challenging task, especially for low-cost OCT systems having low signal-to-noise ratio (SNR). This paper introduces a principled and noise-robust method of detection and estimation of surfaces measured with OCT. We first derive the maximum likelihood estimator (MLE) for the position and reflectivity of a single opaque surface. We next derive a threshold that uses the acquisition noise variance and the number of measurements available to set a target probability for false acceptance of spurious surface estimates. The threshold and MLE are then incorporated into an algorithm that sequentially detects and estimates surface locations. We demonstrate reconstruction of fine details in samples with optical path lengths around 1 mm and depth error down to 1.5 $\mathrm{\mu }$ m despite SNRs as low as –10 dB.