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.
The current color constancy methods are based on an image processing of the sensor's RGB data to estimate the color of illumination. Unlike previous methods, whitebalPR measures the illuminant by separating diffuse and specular components in a scene by taking advantage of the polarizing effect occurring to light reflection. Polarization difference imaging (PDI) detects the polarization degree of the neutrally reflected (specular) parts and eliminates the remitted (diffuse) non-polarized colored parts.
Different experiments explore the signal level within the polarization difference image in relation to multicolored objects, different object surfaces and to the arrangement of light source, camera and object. The results exhibit a high accuracy of measuring the color of illumination for glossy and matte surfaces. As these setups work best for achromatic objects, this new approach for data analysis combines the ideas of the dichromatic reflection model (DRM) and whitebalPR and delivers reliable results for mainly colored objects. Unlike the DRM needs to segment the image referring to the objects in the scene, the new proposal (polarization difference line imaging, PDLI) is independent from any knowledge of the image content. A further arbitrarily segmentation of the image into macro-pixels of any size reduces the computational effort and diminishes the impact of noise on the PDI signal. An according experiment visualizes the coherency between the size of the macro-pixels, the angle of incidence and the accuracy of the process. To sum up, by means of the segmentation the PDLI process gains further stabilization in detecting the color of the illuminant while the computational effort decreases.
This new color constancy method is based on the polarization degree of that light which is reflected at the surface of an object. The subtraction of at least two images taken under different polarization directions detects the polarization degree of the neutrally reflected portions and eliminates the remitted non-polarized colored portions.
Two experiments have been designed to clarify the performance of the procedure, one to multicolored objects and another to objects of different surface characteristics. The results show that the mechanism of eliminating the remitted, non-polarized colored portions of light works very fine. Independent from its color, different color pigments seem to be suitable for measuring the color of the illumination.
The intensity and also the polarization degree of the reflected light depend on the surface properties significantly. The results exhibit a high accuracy of measuring the color of the illumination for glossy and matt surfaces. Only strongly scattering surfaces account for a weak signal level of the difference image and a reduced accuracy.
An embodiment is proposed to integrate the new method into digital cameras.
The study investigates the lossy compression of DSC raw data based upon the 12 bit baseline JPEG compression.
Computational simulations disclose that JPEG artefacts originate from the quantization of the DCT coefficients. Input noise is shown to serve as an appropriate means to avoid these artefacts. Stimulated by such a noise, the JPEG encoder simply acts as an high frequency noise generator.
The processing structure of a general compression model is introduced. The four color planes of an image sensor are separately compressed by a 12 bit baseline JPEG encoder. One-dimensional look-up-tables allow for an optimized adaptation of the JPEG encoder to the noise characteristics of the input signals. An idealized camera model is presumed to be dominated by photon noise. Its noise characteristics can optimally be matched to the JPEG encoder by a common gamma function.
The gamma adapted compression model is applied to an exemplary set of six raw images. Its performance concerning the compression ratio and compression noise is examined.
Optimally adjusted to the input noise, the compression procedure offers excellent image quality without any perceived loss referring to sharpness or noise. The results show that this method is capable to achieve compression ratios of about factor 4 in practice. The PSNR reaches about 60 dB over the complete signal range.