Authors:
Sebastian Merzbach
1
;
Michael Weinmann
1
;
Martin Rump
2
and
Reinhard Klein
1
Affiliations:
1
University of Bonn, Germany
;
2
X-Rite and Inc., United States
Keyword(s):
Spectral, Reflectance, Noise, Spectral Reconstruction.
Related
Ontology
Subjects/Areas/Topics:
Computer Vision, Visualization and Computer Graphics
;
Image-Based Rendering
;
Lighting and Appearance
;
Rendering
Abstract:
In recent years there has been an increasing interest in multispectral imaging hardware. Among many other
applications is the color-correct reproduction of materials. In this paper, we aim at circumventing the limitations
of most devices, namely extensive acquisition times for acceptable signal-to-noise-ratios. For this purpose
we propose a novel approach to spectral imaging that combines high-quality RGB data and spatial filtering of
extremely noisy and sparsely measured spectral information. The capability of handling noisy spectral data
allows a dramatic reduction of overall exposure times. The speed-up we achieve allows for spectral imaging at
practical acquisition times. We use the RGB images for constraining the reconstruction of dense spectral information
from the filtered noisy spectral data. A further important contribution is the extension of a commonly
used radiometric calibration method for determining the camera response in the lowest, noise-dominated range
of pixel valu
es. We apply our approach both to capturing single high-quality spectral images, as well as to
the acquisition of image-based multispectral surface reflectance. Our results demonstrate that we are able to
lower the acquisition times for such multispectral reflectance from several days to the few hours necessary for
an RGB-based measurement.
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