Stray light is the part of an image that is formed by misdirected light. I.e. an ideal optic would map a point of
the scene onto a point of the image. With real optics however, some parts of the light get misdirected. This is
due to effects like scattering at edges, Fresnel reflections at optical surfaces, scattering at parts of the housing,
scattering from dust and imperfections – on and inside of the lenses – and further reasons. These effects lead to
errors in colour-measurements using spectral radiometers and other systems like scanners. Stray light is further
limiting the dynamic range that can be achieved with High-Dynamic-Range-Technologies (HDR) and can lead
to the rejection of cameras due to quality considerations. Therefore it is of interest, to measure, quantify and
correct these effects. Our work aims at measuring the stray light point spread function (stray light PSF) of
a system which is composed of a lens and an imaging sensor. In this paper we present a framework for the
evaluation of PSF-models which can be used for the correction of straylight. We investigate if and how our
evaluation framework can point out errors of these models and how these errors influence straylight correction.
An approach for darksignal-correction is presented that uses a model of each pixel's darksignal, which depends
on the sensor's settings (integration time and gain) and its temperature. It is shown how one can improve the
outcome of such a darksignal-correction strategy by using the darksignal of some pixels in order to compute
an estimate of the sensor's temperature. Experimental results indicate that the darksignals' dependency on
temperature and gain is more complex than considered in up-to-date darksignal models. In this paper it is
shown how one can cope with this complex behaviour when estimating the temperature out of the darksignal.
Experimental results indicate, that our method yields better results than using temperature measurements of
dedicated temperature sensors.
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