We present a new method to estimate color gamut from primary color spectra only. By using this method, the number of
prints and measurements for printer characterization is greatly reduced, which is of particular importance if there are
many ink candidates to be combined. The method consists in building a mapping between primary color spectra and their
combinations, based on a training set. The mapping is data-driven, without relying on assumptions based on classic
theoretical models. Furthermore, we study the effects of smoothing this mapping and its consequences in color
estimation accuracy. We tested the method on recently released Latex-based ink-jet ink, for different pigment and latex
loads. The mean simulation accuracy error, optimizing the smoothing parameter, was below 1.5 dE, and gamut
estimation error below 2% error. For pigment kinds, mean accuracy was below 4 dE, and gamut estimation error about
5%. In all cases, this new method outperforms other overprint estimation methods, such as Kubelka-Munk.
In this paper we present a method to characterize the printer color output with few samples. Color measurements
previously obtained on other substrates and stored in the printer are used to increase the accuracy of measurements in a
new target media, thus reducing the number of samples needed. The method is simple and generic; a geometrical warp is
applied to the color space to adapt the differences between the two media. The warping is built with a small set of
measurements on the target media and extended to the entire color space with bi-harmonic spline interpolation. We
tested the method on a HP T1100 ink-jet printer, at different levels of sampling -from 27 to 512 points- and with seven
different substrate families covering a wide variety of applications. For 125 samples, results show a mean estimation
error across media of mean 0.67 dE76 and 95 percentile 1.48 dE76 with respect to the finer sampling of 512 samples.
This represents an improvement in color accuracy with respect to linear interpolation of about 60%, a relationship holds
at other levels of sampling. In conclusion, color space warping is proven to be an effective method to reduce the needed
color samples by using previously characterized media.
In order to print accurate colors on different substrates, color profiles must be created for each specific ink-media
combination. We tackled the problem of creating such color profiles from only few color samples, in order to
reduce the needed time of operation. Our strategy is to use a spectral reflectance prediction model in order
to estimate a large sampling target (e.g. IT8.7/3) from only a small subset of color patches. In particular,
we focused on the so-called Yule-Nielsen modified Spectral Neugebauer model, proposing new area coverage
estimation, and a prediction of Neugebauer primaries, which can not be directly measured due to ink limiting.
We reviewed the basis of such model, interpret it under the perspective of generalized averaging, and derived
expressions to decouple optical and mechanical dot gain effects. The proposed area coverage estimations are
based on assumptions of the printing process, and characterized through few extra color samples. We tested the
models with thermal ink-jet printers on a variety of media, with dye-based and pigment-based inks. The IT8.7/3
target was predicted from 44 samples, with color average accuracy below 4 dE and maximum error below 8 dE,
for dye-based inks, which performed better than pigment-based inks.
Standard spatial compounding, via averaging acquisitions from different angles, has proved to be an efficient technique for speckle pattern reduction in ultrasound B-mode images. However, the resulting images may be blurred due to the averaging of point spread functions and the misalignment of the different views. These blurring artefacts result in a loss of important anatomical features that may be critical for medical diagnosis. In this paper, we evaluate some spatial compounding techniques, focusing on how to combine the different acquisitions. The evaluated methods are: weighed averaging, wavelet coefficient fusion and multiview deconvolution. To some extent, these techniques take into account the limitations of spatial compounding, by proposing alternative fusion methods that can reduce speckle artefacts while preserving standard spatial resolution and anatomical features.
We experimented these compounding methods with synthetic images to show that these advanced techniques could outperform traditional averaging. In particular, multiview deconvolution techniques performed best, showing improvement in respect to averaging (6.81 dB) for realistic levels of speckle noise and spatial degradation. Wavelet fusion technique ranked second (2.25 dB), and weighted average third (0.70 dB). On the other hand, weighted averaging was the least time consuming, followed by wavelet fusion (x2) and multiview deconvolution (x5). Wavelet fusion offered an interesting trade-off between performance and computational cost.
Experiments on 3D breast ultrasound imaging, showed consistent results with those obtained on synthetic images. Tissue was linearly scanned with a 2D probe in different directions, and volumes were compounded using the aforementioned techniques. This resulted in a high-resolution volume, with better tissue delineation and less speckle patterning.