The laser source with high peak power and high beam quality has important application in laser processing and other fields. A fiber laser with repetition of 50 kHz, pulse width of 3.9 ps, and average power of 10.9 mW is used as the seed source. After double-passing two-stage amplifiers, the average output power of 27.65 W with a peak power of 65 MW is obtained. The first stage is an end pumped Nd:YVO<sub>4</sub> amplifier, and the second stage is a side pumped Nd:YAG amplifier. The beam quality is well preserved with M<sup>2</sup> factor of 1.3 based on the method of spherical-aberration compensation and the optimizing of the beam filling factor.
Proc. SPIE. 6033, ICO20: Illumination, Radiation, and Color Technologies
KEYWORDS: Mathematical modeling, Principal component analysis, Statistical analysis, Detection and tracking algorithms, Scanners, Reflectivity, Color difference, Neural networks, Analytical research, RGB color model
Spectral characterization technique has a prominent advantage that it does not suffer from the problem of metamerism in comparison with Colorimetric characterization methods. PCA (Principle Component Analysis) is an important and useful mathematical method for data reduction, in which a set of spectra, so-called statistical colorants, can be derived from spectral properties of a large set of samples. The spectral reflectance of the color, an admixture of these statistical colorants, can be represented by approximately linear addition of their spectral reflectances. In this paper, a new method for spectral characterization of a flat panel color scanner using PCA method was proposed. Firstly, the PCA algorithm was applied to estimate the spectral reflectance of the statistical colorants on the color targets scanned, and then the colorant scalars were calculated. Secondly, the relationship between the colorant scalars and the scanner RGB signals was built using BP (Back Propagation) neural network. The scanner was characterized also using polynomial regression model and BP neural network directly between scanner RGB values and divice-independent tristimulus values. The experiment results showed that the spectral characterization using PCA method was more accurate than the polynomial regression model and similarly accurate as the direct neural network method but more useful because of the accurate spectral reflectance estimation ability.
CRT color gamut boundaries can be determined by two steps workflow. Firstly, the display should be calibrated with the method recommended by CIE to characterize the relationship between CIE tristimulus values and DAC values. The nonlinear relationship of each electronic channel between the color of the radiant output of CRT displays and the digital DAC values can be characterized accurately with GOG model using parameters of gain, offset, and gamma. Secondly, color gamut boundary can be determined using a fast and accurate algorithm. Generally, in a color space, any chosen degree of lightness will reduce that space to a plane. The color gamut on this equal-lightness plane can be transformed into RGB DAC value space. Since locations on the edges and surfaces of RGB DAC value space will correspond colors with relatively high saturation, the boundary of the curved surface in RGB DAC value space can be quickly computed for certain lightness. The accurate color gamut is obtained by mapping this boundary over to such a perceptual color space as CIELAB or CIELUV uniform color space. The key issue of this algorithm is to compute the equal-lightness curved surface in RGB DAC value space. The resolution of device gamut description depends on the number of segments that the lightness axis is separated into in the perceptual color space.