Accelerating spectral-based color separation within the Neugebauer subspace

Spectral separation is the process of obtaining printer control values to reproduce a given spectral reflectance. Given a multispectral image where each pixel represents a spectral reflectance, separation could be implemented by inverting a physical printer model on a pixel-by-pixel basis. Such a process would obviously need to be very fast to handle high-resolution images in a reasonable time. For a printer whose spectral response is characterized by the Yule–Nielsen spectral Neugebauer model, the linear regression iteration (LRI) method can be used to invert the model. We introduce the subspace linear regression iteration (SLRI) method, a modification of LRI shown to be significantly accelerated due to performing its calculations within the subspace determined by the Neugebauer primaries. Using this subspace approach, the number of multiplications becomes independent of the spectral sampling rate. Using a standard six color printer and a common spectral sampling rate, the number of multiplications can be decreased by about two-thirds without changing the convergence behavior.

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