Closed loop color calibration is a process to maintain consistent color reproduction for
color printers. To perform closed loop color calibration, a pre-designed color target should
be printed, and automatically measured by a color measuring instrument. A low cost sensor
has been embedded to the printer to perform the color measurement. A series of sensor
calibration and color conversion methods have been developed. The purpose is to get
accurate colorimetric measurement from the data measured by the low cost sensor.
In order to get high accuracy colorimetric measurement, we need carefully calibrate the
sensor, and minimize all possible errors during the color conversion. After comparing
several classical color conversion methods, a regression based color conversion method has
been selected. The regression is a powerful method to estimate the color conversion
functions. But the main difficulty to use this method is to find an appropriate function to
describe the relationship between the input and the output data. In this paper, we propose to
use 1D pre-linearization tables to improve the linearity between the input sensor measuring
data and the output colorimetric data. Using this method, we can increase the accuracy of
the regression method, so as to improve the accuracy of the color conversion.
The purpose of printer color calibration is to ensure accurate color reproduction and to maintain the color consistency. A closed loop color calibration system has the ability to perform the whole calibration work automatically without any user intervention. A color calibration process consists of several steps: print a pre-designed target; measure the printed color patches using an color measuring instrument; run a set of algorithms to calibrate the color variations. In this paper, we present a closed loop color calibration system. And we will show in particular how to use a low cost optical sensor to get accurate color measurement. Traditional, low cost optical sensors are only used to measure the voltage data or density data. The novelty of our approach is that we can also use a low cost optical sensor to measure colorimetric data. Using the colorimetric measurement, we can perform more complicate color calibration works for color printing systems.
Stochastic screening technique uses a fixed threshold array to generate halftoned images. When this technique is applied to color images, an important problem is how to generate the masks for different color planes. Ideally, a set of plane dependent color masks should have the following characteristics: a) when total ink coverage is less than 100%, no dots in different colors should overlap from each other. b) for each individual mask, dot distribution should be uniform, c) no visual artifact should be visible due to the low frequency patterns. In this paper, we propose a novel color mask generation method in which the optimal dot placement is searched directly in spatial domain. The advantage of using the spatial domain approach is that we can control directly the dot uniformity during the optimization, and we can also cope with the color plane-dependency by introducing some inter-plane constraints. We will show that using this method, we can generate plane dependent color masks with the characteristics mentioned above.
Color histogram analysis is a powerful tool for characterizing color images. It has been widely used in image indexing and retrieval systems. A key problem to use color histogram in image classification is to find a robust similarity measurement between different color histograms. In this paper, we propose to use a cross-correlation function to measure color histogram similarity. We show that a cross-correlation function has several advantages over the method of histogram intersection, which has been widely used to calculate the similarity between color histograms: A cross-correlation function is normalized automatically; it can determine the similarity irrespective of image size; it is invariant to small color shift; it is easier to implement using the computationally efficient methods. We present an example of unsupervised image clustering by applying cross-correlation function to color histograms. This method was used to improve the perceived color consistency in a multi-print-engine system. We also show how to optimize the cross-correlation function to compensate for the color shift.
Closed loop printer color calibration is a process to ensure consistent color reproduction. For an ink jet printer, the color rendering result may vary due to the manufacturing variations in ink drop weight, ink chemistry, and environmental effects of temperature and humidity on printing process. In this paper, we present a fast printer color calibration method which is designed specifically for mid- to low-end printers. A new linearization method has been proposed. Using this method, we can reduce the number of color patches used for color calibration, whereas maintain the high calibration accuracy. The basic idea is that, for each primary ink (CMYK or CMYKcm), instead of printing and measuring the whole color ramp to generate the linearization table online, we measure only one color patch to estimate the ink drop weight. Then by using a set of pre-build linearization tables, the final linearization table is obtained by interpolating the pre-build tables. We show how to select the ink jet pens with different drop weights to pre-build the linearization tables. We show also how to estimate the ink drop weight and compute the final linearization table by interpolation.
Printer color calibration is a crucial step to ensure consistent color reproduction. In this paper, we present a color calibration system that can not only ensure the color consistency for a same printer at different times, but also ensure the color consistency for different printers of the same model. We will analyze the most significant sources of color variations in an ink jet printing system, and show that some factors produce only the luminance (optical density) variation, and some other factors produce both luminance and chrominance (hue and saturation) variations. Two adequate color calibration methods are proposed to compensate for these variations: one is based on 1D linearization, which is used to compensate for the luminance variation; and the other is based on a 3D search in an existing color conversion table, this method is particularly designed to compensate for the chrominance variation.
A new speckle suppression filter using adaptively tailored windows to preserve edges when they are present is proposed. Results are presented for synthetic aperture radar images and comparison is made with existing speckle filtering methods that do not have the shape adaptivity property.
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