In optical imaging systems, specifically digital cameras, part of the incoming light flux is misdirected to undesired locations due to scattering, undesired reflections, diffraction and lens aberrations. The portion due mainly to scattering and undesired reflections is called stray light. Stray light reduces contrast and causes color inaccuracy in images. The point spread function (PSF) model for stray light is shift variant and has been studied by Jansson et al. (1998) and Bitlis et al. (2007). In this paper, we keep the model's shift variant nature and improve it by first normalizing it and then incorporating the shading effect inherent in the optical system. We then develop an efficient method to estimate the model parameters by using a locally shift invariant approximation. Finally, we
reduce the stray light by deconvolution. We conducted extensive experiments with two camera models. Results from these experiments show the reduction of stray light and thus the improvement of image quality and fidelity.
In any real optical imaging system, some portion of the entering light flux is
misdirected to undesired locations due to scattering from surface imperfections
and multiple reflections between optical elements. This unwanted light is called
stray light. Its effects include lower contrast, reduced detail, and color inaccuracy. Accurate removal of stray-flux effects first requires determination of the stray light point-spread function (PSF) of the system. For digital still cameras,
we assume a parametric, shift-variant, rotationally invariant PSF model. For
collection of data to estimate the parameters of this model, we use a light
source box that provides nearly uniform illumination behind a circular aperture.
Several images of this light source are captured when it is at different locations in the field of view of the camera. Also, another exposure of each
scene with a different shutter speed is used to provide details in the darker
regions. A subset of the data obtained from these images is used in a nonlinear
optimization algorithm. After estimating the parameters of the PSF model, we provide the results of applying the correction algorithm to the images taken of real world scenes.
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