Development of a low-cost, 11 µm spectral domain optical coherence tomography surface profilometry prototype

Suliali, Nyasha J.; Baricholo, Peter; Neethling, Pieter; Rohwer, Erich G.

doi:10.1117/12.2268064

Cited by 1 publication

(2 citation statements)

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“…The first demonstrations of OCT were for ocular imaging [1,2], and the technique has now become standard for that application. Although initially conceived as a method of imaging into an optically transparent volume, OCT has also been used for non-contact profilometry, or measuring the topography of an opaque surface, with applications including dermatology [3], varnish monitoring [4], and component inspection in industrial settings [5,6,7].…”

Section: Introductionmentioning

confidence: 99%

“…Existing Fourier-domain OCT profilometry approaches are limited in robustness due to the fast Fourier transform (FFT)-based processing used for depth recovery [5,6]. Because measurements are made with samples uniformly spaced in wavelength, interpolation is required to convert to samples uniformly spaced in wavenumber before the Fourier transform can be applied, and the interpolation introduces errors for high-frequency interference patterns in the presence of noise.…”

Section: Introductionmentioning

confidence: 99%

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Maximum Likelihood Surface Profilometry Via Optical coherence Tomography

Rapp

Mansour

Boufounos

et al. 2022

2022 IEEE International Conference on Image Processing (ICIP)

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Optical coherence tomography (OCT) using Fourier domain processing 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 paper, we show that surface depth profilometery with a Fourier-domain OCT system simplifies to a sinusoidal parameter estimation problem. We derive approximate maximum likelihood estimators for the sample depth and reflectivity, which can easily be computed by backprojecting the data without interpolating. Iterative refinement 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 experimental 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.

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