In this article, we deal with the problem of spectral reflectance function representation and estimation in the context of multispectral imaging. Because the reconstruction of such functions is an inverse Problem, slight variations in input data completely skew the fore, stabilizing expected results. Therefore, the stabilizing the reconstruction process is necessary. To do this, we propose to use wavelets as basis functions, and we compare those with Fourier and PCA bases. We present the idea and cony)are these three methods, which belong to the class of linear models. The PCA method is training-set dependent and confirms its robustness when applied to reflectance estimation of the training sets. Fourier and wavelet bases allow, good generalization: an advantage of wavelets is that they avoid boundary artifacts. The results are evaluated with the commonly used goodness-of-fit coefficient (GFC), and the reliability of the use of wavelets is proved. (c) 2008 Wiley Periodicals, Inc. Col Res Appl. 33, 485-493, 2008; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/col.20442