Improving reflectance reconstruction from tristimulus values by adaptively combining colorimetric and reflectance similarities

Bin Cao; Ningfang Liao; Yasheng Li; Haobo Cheng

doi:10.1117/1.OE.56.5.053104

13 May 2017 Improving reflectance reconstruction from tristimulus values by adaptively combining colorimetric and reflectance similarities

Bin Cao, Ningfang Liao, Yasheng Li, Haobo Cheng

Author Affiliations +

Optical Engineering, Vol. 56, Issue 5, 053104 (May 2017). https://doi.org/10.1117/1.OE.56.5.053104

Abstract

The use of spectral reflectance as fundamental color information finds application in diverse fields related to imaging. Many approaches use training sets to train the algorithm used for color classification. In this context, we note that the modification of training sets obviously impacts the accuracy of reflectance reconstruction based on classical reflectance reconstruction methods. Different modifying criteria are not always consistent with each other, since they have different emphases; spectral reflectance similarity focuses on the deviation of reconstructed reflectance, whereas colorimetric similarity emphasizes human perception. We present a method to improve the accuracy of the reconstructed spectral reflectance by adaptively combining colorimetric and spectral reflectance similarities. The different exponential factors of the weighting coefficients were investigated. The spectral reflectance reconstructed by the proposed method exhibits considerable improvements in terms of the root-mean-square error and goodness-of-fit coefficient of the spectral reflectance errors as well as color differences under different illuminants. Our method is applicable to diverse areas such as textiles, printing, art, and other industries.

Citation Download Citation

Bin Cao, Ningfang Liao, Yasheng Li, and Haobo Cheng "Improving reflectance reconstruction from tristimulus values by adaptively combining colorimetric and reflectance similarities," Optical Engineering 56(5), 053104 (13 May 2017). https://doi.org/10.1117/1.OE.56.5.053104

Received: 19 October 2016; Accepted: 19 April 2017; Published: 13 May 2017

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