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Volume: 6 | Article ID: art00063
Reflectance recovery using localised weighted method
  DOI :  10.2352/CGIV.2012.6.1.art00063  Published OnlineJanuary 2012

This paper evaluated four conventional methods for reflectance recovery: smoothness method [1], principle component analysis [2], basis functions with smoothness constraint [3] and Wiener estimation [4,5]. Most of these methods adopt a “learning-based” procedure with a training set. Modifications based on the training set were applied for improving the reflectance recovery performance. This paper described combined methods involving the application of localised training data and localised training data with a weighted matrix to the four recovery methods [1-5]. All these methods were applied to recover reflectance from XYZ values for two datasets. Both the training and testing performance were evaluated in terms of CIEDE2000 colour differences. The results showed that the performance of the methods with localised training data significantly improved. There are also limited improvements by applying the weighted matrix. Overall, the localised weighted method (using a local training set with a weighted matrix) with Weiner estimation method performed the best.

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Yi-Fan Chou, Vien Cheung, Changjun Li, M Ronnier Luo, San-Liang Lee, "Reflectance recovery using localised weighted methodin Proc. IS&T CGIV 2012 6th European Conf. on Colour in Graphics, Imaging, and Vision,  2012,  pp 362 - 366,

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