Improved Spectral Density Measurement from Estimated Reflectance Data with Kernel Ridge Regression
Density measurement of printed color samples takes an important role in print quality inspection and process control. When multi-spectral imaging systems are considered for surface reflectance measurement, the possibility of calculating spectral print density over the spatial image domain arises. A drawback in using multi-spectral imaging systems is that some spectral reconstruction algorithms can produce estimated reflectances which contain negative values that are physically not meaningful. When spectral density calculations are considered, the results are erroneous and calculations might even fail in the worst case. We demonstrate how this problem can be avoided by using kernel ridge regression with additional link functions to constrain the estimates to positive values.
Keywordsmulti-spectral imaging spectral density kernel regression
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