Stanford Advisors


All Publications


  • Thin-film interference filters illuminated by tilted apertures Goossens, T., Van Hoof, C. OPTICAL SOC AMER. 2020: A112–A122

    Abstract

    Thin-film interference filters can be illuminated by a circular aperture at different angles. Each situation produces a different transmittance spectrum. We present an analytical model that, for small tilt angles, predicts the change in transmittance for an arbitrary position of the filter in three-dimensional space. The model is extended to take into account higher-order harmonics. We also derive a formula to predict the change in central wavelength, and we validate our results by comparison with thin-film transfer-matrix calculations. A key property of our approach is that the model can be combined with empirical data to predict the transmittance without knowing the filter design.

    View details for DOI 10.1364/AO.59.00A112

    View details for Web of Science ID 000526522300018

    View details for PubMedID 32225362

  • Exit pupil localization to correct spectral shift in thin-film Fabry-Perot spectral cameras OSA CONTINUUM Goossens, T., Van Hoof, C. 2019; 2 (7): 2217–26
  • Vignetted-aperture correction for spectral cameras with integrated thin-film Fabry-Perot filters APPLIED OPTICS Goossens, T., Geelen, B., Lambrechts, A., Van Hoof, C. 2019; 58 (7): 1789–99

    Abstract

    Spectral cameras with integrated thin-film Fabry-Perot filters have become increasingly important in many applications. These applications often require the detection of spectral features at specific wavelengths or to quantify small variations in the spectrum. This can be challenging since thin-film filters are sensitive to the angle of incidence of the light. In prior work, we modeled and corrected for the distribution of incident angles for an ideal finite aperture. Many real lenses, however, experience vignetting. Therefore, in this paper, we generalize our model to the more common case of a vignetted aperture, which changes the distribution of incident angles. We propose a practical method to estimate the model parameters and correct undesired shifts in measured spectra. This is experimentally validated for a lens mounted on a visible-to-near-infrared spectral camera.

    View details for DOI 10.1364/AO.58.001789

    View details for Web of Science ID 000460120600033

    View details for PubMedID 30874220

  • Finite aperture correction for spectral cameras with integrated thin-film Fabry-Perot filters APPLIED OPTICS Goossens, T., Geelen, B., Pichette, J., Lambrechts, A., Van Hoof, C. 2018; 57 (26): 7539–49

    Abstract

    Spectral cameras with integrated thin-film Fabry-Perot filters enable many different applications. Some applications require the detection of spectral features that are only visible at specific wavelengths, and some need to quantify small spectral differences that are undetectable with RGB color cameras. One factor that influences the central wavelength of thin-film filters is the angle of incidence. Therefore, when light is focused from an imaging lens onto the filter array, undesirable shifts in the measured spectra are observed. These shifts limit the use of the sensor in applications that require fast lenses or lenses with large chief ray angles. To increase flexibility and enable new applications, we derive an analytical model that explains and can correct the observed shifts in measured spectra. The model includes the size of the aperture and physical position of each filter on the sensor. We experimentally validate the model with two spectral cameras: one in the visible and near-infrared region and one in the short wave infrared region.

    View details for DOI 10.1364/AO.57.007539

    View details for Web of Science ID 000444085500015

    View details for PubMedID 30461824

  • Hyperspectral Calibration Method For CMOS-based Hyperspectral Sensors Pichette, J., Goossens, T., Vunckx, K., Lambrechts, A., Soskind, Y. G., Olson, C. SPIE-INT SOC OPTICAL ENGINEERING. 2017

    View details for DOI 10.1117/12.2253617

    View details for Web of Science ID 000399923700014

  • Block-Decoupling Multivariate Polynomials Using the Tensor Block-Term Decomposition Dreesen, P., Goossens, T., Ishteva, M., De Lathauwer, L., Schoukens, J., Vincent, E., Yeredor, A., Koldovsky, Z., Tichavsky, P. SPRINGER-VERLAG BERLIN. 2015: 14–21