1 April 2010 Estimating reflectance from multispectral camera responses based on partial least-squares regression
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J. of Electronic Imaging, 19(2), 020501 (2010). doi:10.1117/1.3385782
Abstract
In multispectral imaging systems, the accuracy of reflectance estimation can be degraded by the nonlinearity in imaging process, which is due to non-Gaussian distribution of the data and nonlinear optoelectronic conversion function of the camera. To deal with nonlinearity, we propose to extend camera responses by high-order polynomials and reduce the overfitting problem by partial least-squares (PLS) regression. Experiment shows that, in terms of both spectral and colorimetric error metrics, the proposed method performs better than Wiener estimation and ordinary polynomial regression, and is similar to polynomial regression with regularization.
Hui-Liang Shen, Hui-Jiang Wan, Zhe-Chao Zhang, "Estimating reflectance from multispectral camera responses based on partial least-squares regression," Journal of Electronic Imaging 19(2), 020501 (1 April 2010). http://dx.doi.org/10.1117/1.3385782
Submission: Received ; Accepted
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KEYWORDS
Reflectivity

Cameras

Error analysis

Imaging systems

Multispectral imaging

Matrices

Data conversion

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