Markus Schnitzlein

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The performance of learning-based spectral estimation is greatly influenced by the set of training samples selected to create the reconstruction model. Training sample selection schemes can be categorized into global and local approaches. Most of the previously proposed global training schemes aim to reduce the number of training samples, or a selection of(More)
This paper is aiming at improving the top view book scanner functionalities to the ability of depicting homogeneous sharpness across the output colour image. Typically, the opened book has a curved shape which results in a space-variant blur in the recorded image. A priori calibration filters are computed by taking an advantage of longitudinal chromatic(More)
We have analyzed the performance of simulated multispectral systems for the spectral recovery of reflec-tance of printer inks from camera responses, including noise. To estimate reflectance we compared the performance of four algorithms which were not comparatively tested using the same data sets before. The criteria for selection of the algorithms were(More)
We evaluate a new 12-channel multi-spectral line scan camera system for full-width inline color measurements. The system provides full reflectance spectra for each pixel allowing for accurate color measurement with high spatial resolution. In our analysis, we performed measurements on three test-charts printed in our lab and by different offset printing(More)
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