17 December 2015 Spectral image reconstruction through the PCA transform
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Proceedings Volume 9811, MIPPR 2015: Multispectral Image Acquisition, Processing, and Analysis; 98110C (2015) https://doi.org/10.1117/12.2204745
Event: Ninth International Symposium on Multispectral Image Processing and Pattern Recognition (MIPPR2015), 2015, Enshi, China
Abstract
Digital color image reproduction based on spectral information has become a field of much interest and practical importance in recent years. The representation of color in digital form with multi-band images is not very accurate, hence the use of spectral image is justified. Reconstructing high-dimensional spectral reflectance images from relatively low-dimensional camera signals is generally an ill-posed problem. The aim of this study is to use the Principal component analysis (PCA) transform in spectral reflectance images reconstruction. The performance is evaluated by the mean, median and standard deviation of color difference values. The values of mean, median and standard deviation of root mean square (GFC) errors between the reconstructed and the actual spectral image were also calculated. Simulation experiments conducted on a six-channel camera system and on spectral test images show the performance of the suggested method.
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Long Ma, Long Ma, Xuewei Qiu, Xuewei Qiu, Yangming Cong, Yangming Cong, } "Spectral image reconstruction through the PCA transform", Proc. SPIE 9811, MIPPR 2015: Multispectral Image Acquisition, Processing, and Analysis, 98110C (17 December 2015); doi: 10.1117/12.2204745; https://doi.org/10.1117/12.2204745
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