Estimating reflectance from multispectral camera responses based on partial least-squares regression

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.