Practical Camera Sensor Spectral Response and Uncertainty Estimation

Measuring the spectral response of a division-of-focal-plane polarization imager using a grating monochromator

Spectral characterizations are performed on imagers to obtain a relative spectral response (RSR) curve. This process often utilizes a grating monochromator with an output that changes polarization as a function of wavelength (our monochromator's degree of linear polarization was found to vary from less than 10% to more than 70%). When characterizing a polarization-sensitive imager, this introduces polarization artifacts into the RSR curve. We present a simple method to avoid these polarization artifacts for division-of-focal-plane polarization imagers by directly illuminating the camera with the monochromator output and calculating the S₀ Stokes parameter at each super pixel, then we show consistent results from this method for two division-of-focal-plane polarization imagers. We also show that ignoring the monochromator polarization results in order-of-magnitude RSR errors. The recommended method uses an iris to limit the spatial extent of the monochromator output, which was found experimentally to increase the minimum signal-to-noise ratio by more than a factor of 2.

Visible Light Spectrum Extraction from Diffraction Images by Deconvolution and the Cepstrum

Accurate color determination in variable lighting conditions is difficult and requires special devices. We considered the task of extracting the visible light spectrum using ordinary camera sensors, to facilitate low-cost color measurements using consumer equipment. The approach uses a diffractive element attached to a standard camera and a computational algorithm for forming the light spectrum from the resulting diffraction images. We present two machine learning algorithms for this task, based on alternative processing pipelines using deconvolution and cepstrum operations, respectively. The proposed methods were trained and evaluated on diffraction images collected using three cameras and three illuminants to demonstrate the generality of the approach, measuring the quality by comparing the recovered spectra against ground truth measurements collected using a hyperspectral camera. We show that the proposed methods are able to reconstruct the spectrum, and, consequently, the color, with fairly good accuracy in all conditions, but the exact accuracy depends on the specific camera and lighting conditions. The testing procedure followed in our experiments suggests a high degree of confidence in the generalizability of our results; the method works well even for a new illuminant not seen in the development phase.