Abstract
In spectral color reproduction workflow, it is of key importance to reconstruct the spectral reflectance of a surface using digital cameras under given luminance and observation conditions. A new approach for solving the problem which is based on neural network and basis vectors is proposed. Compared with other traditional methods, neural network expands the space of unknown function from linear functions to more general nonlinear functions, which gives more accurate estimation of the coefficients and better reflectance reconstruction. Results show that the reflectance of standard Munsell color patch (Matte) can be reconstructed. Compared with linear approximation method, reconstruction of standard Munsell color patch (Matte) using this approach reduces the reconstruction error. Therefore, we conclude that this approach has advantages of higher accuracy, fast implementation, and adaptation, thus can be used in arts reproduction and museum art collection, etc.
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Acknowledgments
The work is supported by Research on Spectral-based Separation relating human perception under multiple light source of Education Department of ZheJiang Province (No. Y201432475), and Research on Technology Integration Standards and Specifications of Cross-media Digital Publishing (No. KYZ223613001), and the Technology Innovation Team of Cross-media Digital Publishing Platform (No. ZX140206320005).
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Yang, P., Huang, S., Qiu, W., Ma, Q., Wang, Q., Song, H. (2016). A Method for Reconstructing Surface Spectral Reflectance in Spectral Reproduction Workflow. In: Ouyang, Y., Xu, M., Yang, L., Ouyang, Y. (eds) Advanced Graphic Communications, Packaging Technology and Materials. Lecture Notes in Electrical Engineering, vol 369. Springer, Singapore. https://doi.org/10.1007/978-981-10-0072-0_5
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DOI: https://doi.org/10.1007/978-981-10-0072-0_5
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