Toward non-metameric reflectance recovery by emulating the spectral neighborhood using corresponding color information

Abstract

In learning-based reflectance reconstruction methods, usually localized training samples are used to reconstruct spectral curves. The state-of-the-art methods localize the training samples based on their colorimetric color differences with the test sample. This approach is dependent on the working color space, color difference equation, and/or illuminant used, and it may result in a metameric match. This issue can be resolved by localizing the training samples based on their spectral difference with the test sample; however, this would require an already unknown spectral curve of the test sample. In this paper, use of corresponding color information to emulate the spectral neighborhood of the test color for non-metameric reflectance recovery is proposed. The Wiener estimation method was extended by (1) using two thresholds, (i) on the color difference between the test sample and the training samples under the reference illuminant and (ii) on the color difference between the corresponding color of the test sample and the training samples under another illuminant, to mimic the spectral neighborhood of the test sample within the gamut of the training data, and (2) also using the tristimulus values of the corresponding color in the regression. Results showed that the proposed extension of the Wiener estimation method improved the reflectance recovery and hence reduced the metamerism.