Maximum Likelihood Surface Profilometry via Optical Coherence Tomography


Optical coherence tomography (OCT) using Fourier domain pro- cessing can resolve micrometer-scale depth information. However, the conventional volumetric reconstruction approach is unnecessary for opaque samples with only one reflector per lateral position, and the required sample interpolation degrades performance. In this pa- per, we show that surface depth profilometery with a Fourier-domain OCT system simplifies to a sinusoidal parameter estimation prob- lem. We derive approximate maximum likelihood estimators for the sample depth and reflectivity, which can easily be computed by backprojecting the data without interpolating. Iterative refine- ment further improves results at high signal-to-noise ratio (SNR). We demonstrate the performance of the technique compared to the conventional Fourier transform approach on both simulated and ex- perimental data collected with a spectral-domain OCT system. Our results show that maximum likelihood profilometry is fast and more robust to noise than the Fourier approaches at moderate SNR.