Fast model-based multispectral imaging using nonnegative principal component analysis.
Estimation of the spectral reflectance of a scene is a critical problem in image processing and computer vision applications. Model-based multispectral imaging, one of the spectral reflectance estimation methods, can effectively reconstruct the full spectrum using a small number of camera shots. However, it is based on iterative optimization and, thus, is computationally too intensive. In this Letter, we modify the iterative optimization problem to a closed-form problem using nonnegative principal component analysis. The proposed method can substantially reduce the computational cost while maintaining the accuracy.
Department of Electronics and Computer Engineering, Hanyang University, 17 Haengdang-dong, Seongdong-ku, Seoul 133-791, South Korea.
Moon-Hyun
MH
Lee
Hanhoon
H
Park
In
I
Ryu
Jong-Il
JI
Park
D007091:Image Processing, Computer-Assisted; D016014:Linear Models; D025341:Principal Component Analysis; D013057:Spectrum Analysis; D013997:Time Factors