Transmission filters forming orthogonal basis for spectral imaging purposes.

Hyperspectral imaging has become a common technique in many different applications, enabling accurate identification of materials based on their optical properties; however, it requires complex and expensive technical implementation. A less expensive way to produce spectral data, spectral estimation, suffers from complex mathematics and limited accuracy. We introduce a novel, to the best of our knowledge, method where spectral reflectance curves can be reconstructed from the measured camera responses without complex mathematics.

Solving the inverse grating problem with the naked eye.

We make use of the color sensitivity of the naked human eye to solve the inverse grating problem. We conduct color-matching experiments between simulated colors and the color of the zero diffraction order, and show that human color vision may reveal structure dimensions at an accuracy in the order of ten nanometers, which is comparable to the precision of destructive methods such as scanning electron microscopy. Our results suggest that for a wide range of structures, the color observation may help to get quick, but still accurate, results, without any sophisticated instrumentation.

Eigenvectors of optimal color spectra.

Principal component analysis (PCA) and weighted PCA were applied to spectra of optimal colors belonging to the outer surface of the object-color solid or to so-called MacAdam limits. The correlation matrix formed from this data is a circulant matrix whose biggest eigenvalue is simple and the corresponding eigenvector is constant. All other eigenvalues are double, and the eigenvectors can be expressed with trigonometric functions.

Number of colors generated by smooth nonfluorescent reflectance spectra.

In this study, we have analyzed statistical properties of the values of the first- and second-order derivatives of spectral reflectance curves. We show that values of all four tested spectral data sets have very similar statistical properties. We set outer limits that bound the clear majority of the values of the first- and second-order derivatives.

Weighted compression of spectral color information.

Spectral color information is used nowadays in many different applications. Accurate spectral images are usually very large files, but a proper compression method can reduce needed storage space remarkably with a minimum loss of information. In this paper we introduce a principal component analysis (PCA) -based compression method of spectral color information.