Spectral Reflection Prediction Research Articles

Nonlinear model prediction of needle chlorophyll content of Picea koraiensis Nakai at different needle ages based on hyperspectral features

Pigment content is a critical assessment indicator in the study of plant physiological metabolism, stress resistance, ornamental characteristics, and forest health. Spectral imaging technology is widely used for rapid and non-destructive determination of plant physicochemical parameters. To address the shortcomings of previous models of spectral reflectance prediction of chlorophyll content of needles only from the perspective of traditional algorithms and ignoring physical models, this research integrates variable complexity and refined classification of physical models to validate the increased accuracy of both the conventional partial least squares (PLS) method and the traditional neural network algorithm. The results of the conifer chlorophyll models of Picea koraiensis Nakai with different needle ages based on spectral reflectance and vegetation index parameters showed that the improved nonlinear state transition algorithm-backpropagation (STA-BP) neural network model approach (R2 of 0.73–0.89) and the nonlinear Stacking partial least squares (Stacking-PLS) model approach (R2 of 0. 67–0.85) is slightly more robust than the traditional algorithms nonlinear BP model (R2 of 0.63–0.82) and linear PLS model (R2 of 0.60–0.76). This finding suggests that the nonlinear fitting of chlorophyll content in needles of different needle ages in P. koraiensis Nakai surpasses the traditional linear model fitting methodology. Furthermore, the model fitting of chlorophyll content in conifers of different needle ages outperforms the mixed P. koraiensis Nakai model, suggesting that chlorophyll models using needle refinement classification help to improve model robustness. This study provides data and theoretical support for rapid and non-invasive characterization of physiological and biochemical properties of needles of different needle ages using spectral imaging techniques to predict growth and community structure productivity of forest trees in the coming years.

Modified Spectral Neugebauer Model for Printer Characterization

Spectral Neugebauer model is widely used for spectral reflectance prediction during printer characterization. However, several factors reduce the predication precision. Thus, an improved cellular Yule-Nielson spectral Neugebauer (CYNSN) model is proposed, which modifies the traditional spectral Neugebauer model in three main aspects: (1) First, in order to adjust the nonlinearities between the predicated and measured spectral reflectance, an iteratively calculated Yule-Nielson exponent is added to the reflectance values within the Neugebauer equations. (2) Second, the quantity of Neugebauer primaries is increased by dividing the CMY colorant space into 43 uniform cellular subdomains. (3) Third, the mapping functions are developed to map the area coverages' theoretical values to their effective values within the subdomains, and the mapped values highly improve the matching degree of the predicated and measured reflectance values. In the experiment, four related spectral Neugebauer models are employed during printer characterization, which are the traditional spectral Neugebauer model, Yule-Nielson spectral Neugebauer (YNSN) model, traditional CYNSN model, and the modified CYNSN model, respectively. And the experimental results show the modified CYNSN model yields a significant improvement over the other spectral Neugebauer models, by comparing the characterization errors in the form of colorimetry and spectroscopy.

Recovering neugebauer colorant reflectances and Ink‐spreading curves from printed color images

Spectral reflection prediction models, although effective, are impractical for certain industrial applications such as self‐calibrating devices and online monitoring because their calibration requires specific color‐constant calibration patches. Using the CMYK Ink‐Spreading enhanced Yule‐Nielsen‐modified Spectral Neugebauer model (IS‐YNSN), we propose a method to recover the colorant reflectances (Neugebauer primaries), the ink‐spreading curves, and the Yule‐Nielsen n‐value using only tiles extracted from printed color images. There is no prior knowledge about the reproduction device. Thanks to a set of constraints based on principal component analysis and the relationships between composed Neugebauer primaries and the ink transmittances, good approximations of the Neugebauer primaries are achieved. These approximations are then optimized, yielding an accurately calibrated IS‐YNSN model comparable to the one obtained by classical calibrations. © 2013 Wiley Periodicals, Inc. Col Res Appl, 39, 216–233, 2014; Published online 23 February 2013 in Wiley Online Library (wileyonlinelibrary.com). DOI 10.1002/col.21800

Calibrating ink spreading curves by optimal selection of tiles from printed color images

The Yule-Nielsen modified spectral Neugebauer model (YNSN) enables predicting reflectance spectra from ink surface coverages of halftones. In order to provide an improved prediction accuracy, this model is enhanced with an ink spreading model accounting for ink spreading in all superposition conditions (IS-YNSN). As any other spectral reflection prediction model, the IS-YNSN model is conceived to predict the reflection spectra of color-constant patches. Instead of color-constant patches, we investigate if tiles located within color images can be accurately predicted and how they can be used to facilitate the calibration of the ink spreading model. In the present contribution, we detail an algorithm to automatically select image tiles as uniform as possible from color images by relying on their CMY or CMYK pixel values. The tile selection algorithm incorporates additional constraints relying on surface coverages of the inks. We demonstrate that an ink spreading model calibrated with as few as 5 to 10 optimally chosen image tiles allows the corresponding YNSN model to provide accurate spectral predictions.

Enhancing the Specular Effect of Metallic Color Prints by Reducing the Use of Yellow Ink

The MetallicArt technique is used as a visual security feature to hide patterns in CMY images using an additional silver ink. The patterns are hidden under nonspecular viewing conditions and visible under specular viewing conditions. By varying the amount of silver ink, the authors control the intensity of the specular reflection of the printed pattern. Using a spectral reflection prediction model, they can constrain the maximization of the silver ink (S) in order to keep the color difference between the original CMY color and the fitted CMYS color under a given threshold, ensuring that the pattern is hidden under nonspecular viewing conditions. When the printing device requires the inks to be UV cured, the leafing effect of the silver ink is reduced. This also reduces the specular reflection of the silver ink, which becomes comparable to the specular reflection of the yellow ink. Since both specular reflections are similar, it is difficult to distinguish the embedded patterns under specular viewing conditions when both inks are present. The authors, therefore, propose a new algorithm to embed patterns in CMY images that not only maximizes silver but also minimizes yellow. Experimental results show that the proposed algorithm enhances the visibility of the patterns under specular viewing conditions, allowing the use of MetallicArt when the printing device requires UV curing. (C) 2011 Society for Imaging Science and Technology [DOI: 10.2352/J.ImagingSci.Technol.2011.55.6.060506]

Constrained Acquisition of Ink Spreading Curves From Printed Color Images

Today's spectral reflection prediction models are able to predict the reflection spectra of printed color images with an accuracy as high as the reproduction variability allows. However, to calibrate such models, special uniform calibration patches need to be printed. These calibration patches use space and have to be removed from the final product. The present contribution shows how to deduce the ink spreading behavior of the color halftones from spectral reflectances acquired within printed color images. Image tiles of a color as uniform as possible are selected within the printed images. The ink spreading behavior is fitted by relying on the spectral reflectances of the selected image tiles. A relevance metric specifies the impact of each ink spreading curve on the selected image tiles. These relevance metrics are used to constrain the corresponding ink spreading curves. Experiments performed on an inkjet printer demonstrate that the new constraint-based calibration of the spectral reflection prediction model performs well when predicting color halftones significantly different from the selected image tiles. For some prints, the proposed image based model calibration is more accurate than a classical calibration.

Deducing ink thickness variations by a spectral prediction model

AbstractMost existing techniques for regulating the ink flow in offset presses rely on density measurements carried out on specially printed patches. In the present contribution, we develop a methodology to deduce ink thickness variations from spectral measurements of multichromatic halftone patches located within the printed page. For this purpose, we extend the Clapper‐Yule spectral reflectance prediction model by expressing the transmittance of the colorants composed of superposed inks as a function of the ink transmittances and of fitted ink layer thicknesses. We associate to each ink an ink thickness variation factor. At print time, this ink thickness variation factor can be fitted to minimize a difference metric between predicted reflection spectrum and measured reflection spectrum. The ink thickness variations deduced from multichromatic halftones allow to clearly distinguish between normal ink volume, reduced ink volume, or increased ink volume. This information can then be used for performing control operations on the printing press. © 2009 Wiley Periodicals, Inc. Col Res Appl, 34, 432–442, 2009

Spectral prediction model for color prints on paper with fluorescent additives

I propose a model for predicting the total reflectance of color halftones printed on paper incorporating fluorescent brighteners. The total reflectance is modeled as the additive superposition of the relative fluorescent emission and the pure reflectance of the color print. The fluorescent emission prediction model accounts for both the attenuation of light by the halftone within the excitation wavelength range and for the attenuation of the fluorescent emission by the same halftone within the emission wavelength range. The model's calibration relies on reflectance measurements of the optically brightened paper and of the solid colorant patches with two illuminants, one including and one excluding the UV components. The part of the model predicting the pure reflectance relies on an ink-spreading extended Clapper-Yule model. On uniformly distributed surface coverages of cyan, magenta, and yellow halftone patches, the proposed model predicts the relative fluorescent emission with a high accuracy (mean DeltaE(94)=0.42 under a D65 standard illuminant). For optically brightened paper exhibiting a moderate fluorescence, the total reflectance prediction improves the spectral reflectance prediction mainly for highlight color halftones, comprising a proportion of paper white above 12%. Applications include the creation of improved printer characterization tables for color management purposes and the prediction of color gamuts for new combinations of optically brightened papers and inks.

Spectral reflection and dot surface prediction models for color halftone prints

The proposed new spectral reflection model enhances the classical Clapper-Yule model by taking into account the fact that proportionally more incident light through a given colorant surface is reflected back onto the same colorant surface than onto other colorant surfaces. It comprises a weighted mean between a component specifying the part of the incident light that exits through the same colorant as the colorant from which it enters (Saunderson corrected Neugebauer component) and a component specifying the part of the incident light whose emerging light components exit from all colorants (Clapper-Yule component). We also propose models for taking into account ink spreading, a phenomenon that occurs when printing an ink halftone in superposition with one or several solid inks. The ink-spreading model incorporates nominal-to-effective surface coverage functions for each of the different ink superposition conditions. A system of equations yields the effective ink surface coverages of a color halftone as a weighted mean of the ink surface coverages specific to the different superposition conditions. The new spectral reflection prediction model combined with the ink-spreading model yields excellent spectral reflection predictions for clustered-dot color halftones printed on an offset press or on thermal transfer printers.