Abstract

Spectral reflectance is defined as the “fingerprint” of an object and is illumination invariant. It has many applications in color reproduction, imaging, computer vision, and computer graphics. In previous reflectance reconstruction methods, spectral reflectance has been treated equally over the whole wavelength. However, human eyes or sensors in an imaging device usually have different weights over different wavelengths. We propose a novel method to reconstruct reflectance, considering a wavelength-sensitive function (WSF) that is constructed from sensor-sensitive functions (or color matching functions). Our main idea is to achieve more accurate reconstruction at wavelengths where sensors have high sensitivities. This more accurate reconstruction can achieve better imaging or color reproduction performance. In our method, we generate a matrix through the Hadamard product of the reflectance matrix and the WSF matrix. We then obtain reconstructed reflectance by applying the singular value decomposition on the generated matrix. The experimental results show that our method can reduce 47% mean-square error and 55% Lab error compared with the classical principal component analysis method.

© 2013 Optical Society of America

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