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To improve the transform accuracy of colorimetric values for digital colour reproduction, a neural network approach based on a large-scale spectral dataset was proposed. The presented dataset exhibited a particularly wide colour gamut and was partitioned into 12 subsets according to dominant wavelength and excitation purity. In each subset, the non-linearity between colorimetric values under source and target illuminant–observer combinations was simulated by a backpropagation neural network. The colorimetric transform accuracy of this approach was compared with several existing methods. The experimental results indicated that the proposed approach significantly enhanced the transform precision for colorimetric values, especially for the colours located in highly saturated regions.

The spectral reconstruction precision of multi-channel spectral acquisition system based on the commercial digital camera is greatly dependent on the filters used to acquire the image. In order to obtain optimal filter pair, which has excellent spectral reconstruction performance and is applicable to all image scenes, a color filter design method based on the principal component analysis is proposed in this work. Firstly, a number of widely-used color filters are collected and their transmittances are measured and then written in matrix form. Secondly, the proposed method is adopted to extract the first two principal components of the matrix. The normalized principal components are the transmittances of the desired synthetic filters. To compare the spectral reconstruction performance of the synthetic filters with the other filter pair obtained by a common used method, two virtual spectral acquisition systems were constructed with two pairs of filters. The performance of each system was evaluated in terms of chromatic difference and spectral root mean square error. The results indicate that the synthetic filters obtained by the proposed method have a better performance than the common used methods. In addition, the performance of the synthetic filters is stable when the target image scenes change.

Digitization of cultural heritage protection has received considerable attention in heritage studies and spectral imaging technology has been playing an important role in this research. This article aims to study the technique of selecting optimal filter set to obtain ancient murals spectral image with high spectral and colorimetric accuracy based on the broadband spectral imaging system. The 330 Dunhuang murals mineral pigment color patches and the GretagMacbeth ColorChecker (CC) as well as 27 pieces of optical filters chosen as samples were examined. For each piece of filter, the three-channel image was captured by the spectral imaging system. Then, 351 groups of six-channel digital count images were acquired by arbitrary combinations of two among the 27 three-channel digital count images. The pseudo-inverse, principal component analysis, and R-matrix methods were used to reconstruct the spectral reflectance from the six-channel digital counts for each sample. Finally, this study identified the optimal filter set by evaluating the integrated error (TOTAL ERROR), which was calculated by normalizing the mean spectral root-mean-square error (RMS), mean spectral goodness-of-fit error (1-GFC), and mean CIEDE2000 color difference (ΔE00) and by multiplying them together. After the optimal optic filter set was selected, it was applied to the Dunhuang murals spectral imaging and was evaluated. The results showed that the optimal optic filter set could result in promising improvement both in spectral and color accuracy when compared with the production camera. In addition, it can be used for the construction of Dunhuang murals spectral image database. © 2015 Wiley Periodicals, Inc. Col Res Appl, 2015