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We propose a color management system for the color facsimile. It consists of protocols for the colorimetric calibration of the scanner and the printer by establishing the relationships between the device-dependent color coordinates and the device-independent CIELAB color space. The scanner calibration is based on 3rd order polynomial regression tec...
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A key problem in multimedia systems is the faithful reproduction of color. One of the main reasons why this is a complicated issue is the different color reproduction technologies used by the various devices; displays use easily modeled additive color mixing, while printers use a subtractive process, the characterization of which is much more compl...
In this study, the problem of updating a printer character- ization in response to systematic changes in print-device characteristics is addressed with two distinct approaches: the creation of corrective models used in conjunction with an existing device model, and the re-evaluation of regression-model parameters using an augmented char- acterizati...
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Citations
... regularly. Vrhel and Trussel [7] (1999) presented the mathematical formulation of calibrating color scanners and found that the mapping from scanned values to colorimetric values is nonlinear. They applied artificial neural network for calibration, then compared this method with other calibration methods based on a test performed with 264 samples. Hardeberg et al (1996) propose an analytic method based on 3 rd order polynomial regression techniques[8]. They used CIE color space values and scanned values of 288 parts of the IT8.7/2 color calibration card. They found out that the polynomial regression delivers better results than other methods. Finlayson and Drew (1997) mentioned that the color values me ...
... They applied artificial neural network for calibration, then compared this method with other calibration methods based on a test performed with 264 samples. Hardeberg et al (1996) propose an analytic method based on 3 rd order polynomial regression techniques[8]. They used CIE color space values and scanned values of 288 parts of the IT8.7/2 color calibration card. ...
... Nous retrouvons des articles utilisant la régression polynomiale et d'autres approches non-linéaire avec notamment l'utilisation d'un réseau de neurone pour eectuer la régression. Nous citons les articles suivants en ce qui concerne la régression polynomiale : [Hardeberg et al., 1996], [Kang, 1997], [Hardeberg, 2001] et [Yilmaz et al., 2004]. Les diérents articles se distinguent par les diérentes mires utilisées. ...
... Ils mettent en évidence que la méthode donne des résultats convaincants mais que le degré du polynôme inue sur la précision des couleurs. Par exemple, dans l'article de Hardeberg et al [Hardeberg et al., 1996], ils mettent en avant que l'utilisation d'un polynôme de degré 3 est plus pertinent qu'un polynôme de degré 2 pour le cas de la mire it8 qui est une mire composée de 290 patchs. Par la suite, nous retrouvons dans sa thèse [Hardeberg, 2001] que la précision de la transformation est très inuencée par la correction gamma. ...
L'objectif de cette thèse est de proposer des méthodes ou des outils de calibration permettant d'améliorer la qualité d'image dans le cadre de la numérisation de livres anciens.Le premier chapitre concerne la mise en oeuvre d'un "scanner sans éclairage". Ce scanner est matriciel et il ne contrôle pas la lumière via un éclairage artificiel. L'objectif du projet est de pouvoir corriger, par calibration ou par la mise en place d'un traitement d'image les défauts d'éclairage apparaissant sur le document. Nous verrons dans un premier chapitre les solutions possibles pour y répondre. Nous y proposerons également un moyen de segmenter le fond du document en se basant sur des pixels caractérisants le fond du document. Ce résultat permettra de reconstruire le fond puis de corriger les non-uniformités.Le deuxième chapitre concerne la mise en place d'un contrôle qualité de numérisation de livres anciens. Effectivement, la manipulation du matériel dans le contexte de la numérisation industrielle engendre des erreurs possibles de réglage. De ce fait, nous obtenons une numérisation de moindres qualités. Le contrôle se focalisera sur le flou de focus qui est le défaut le plus présent. Nous proposons dans ce chapitre de combiner différentes approches du problème permettant de le quantifier. Nous verrons que la combinaison de ces informations permet d'estimer avec précision la qualité de netteté de l'image.Le troisième chapitre évoque les problématiques de la gestion de la couleur. Pour avoir une homogénéité colorimétrique entre tous les appareils, il est indispensable que la calibration soit inférieure à un seuil visuel. Les contraintes industrielles ont de nombreux critères et il est difficile de tous les respecter. L'objectif de ce chapitre est de récapituler comment mettre en place un système permettant de calibrer la couleur avec toutes ces contraintes. Puis, il s'agit de comprendre comment créer une transformation entre l'espace de couleur de l'appareil et l'espace de couleur de connexion (l'espace L*a*b*). Nous verrons que la solution de ce problème se résout par une régression polynomiale dont le degré du polynôme varie en fonction du nombre de mesures faite sur la mire colorimétrique.
... regularly. Vrhel and Trussel [7] (1999) presented the mathematical formulation of calibrating color scanners and found that the mapping from scanned values to colorimetric values is nonlinear. They applied artificial neural network for calibration, then compared this method with other calibration methods based on a test performed with 264 samples. Hardeberg et al (1996) propose an analytic method based on 3 rd order polynomial regression techniques[8]. They used CIE color space values and scanned values of 288 parts of the IT8.7/2 color calibration card. They found out that the polynomial regression delivers better results than other methods. Finlayson and Drew (1997) mentioned that the color values me ...
... They applied artificial neural network for calibration, then compared this method with other calibration methods based on a test performed with 264 samples. Hardeberg et al (1996) propose an analytic method based on 3 rd order polynomial regression techniques[8]. They used CIE color space values and scanned values of 288 parts of the IT8.7/2 color calibration card. ...
... regularly. Vrhel and Trussel [7] (1999) presented the mathematical formulation of calibrating color scanners and found that the mapping from scanned values to colorimetric values is nonlinear. They applied artificial neural network for calibration, then compared this method with other calibration methods based on a test performed with 264 samples. Hardeberg et al (1996) propose an analytic method based on 3 rd order polynomial regression techniques[8]. They used CIE color space values and scanned values of 288 parts of the IT8.7/2 color calibration card. They found out that the polynomial regression delivers better results than other methods. Finlayson and Drew (1997) mentioned that the color values me ...
... They applied artificial neural network for calibration, then compared this method with other calibration methods based on a test performed with 264 samples. Hardeberg et al (1996) propose an analytic method based on 3 rd order polynomial regression techniques[8]. They used CIE color space values and scanned values of 288 parts of the IT8.7/2 color calibration card. ...
Characterization and estimation of device independent color co-ordinates of scanned or photographic images
of through empirical models is a typical issue for commercial desktop scanners and cameras. As a domain of complex problem for optimising suitable color model to evaluate textiles and paints, we investigated the effect of different DPI and device independent color coordinate selection for color Characterization using 140 color patch standards of SGA Colorchecker on Fabric Eye D 2000 scanner. This paper extensively discusses the polynomial regression and neural network characterization techniques and their respective model performances in order to relate typical scanning devices to spectrophotometer i.e., get CIE XYZ/ L*a*b*/L*c*h* from RGB values of scanned image of for D65 10 illuminant and observer.
... To convert the camera output signals to deviceindependent data, two main approaches are proposed and evaluated. One consists of applying an extended version of the colorimetric scanner characterization method proposed previously 24,[30][31][32] to convert from K different color channels (KϾ3) to a 3-D color space such as CIEXYZ or CIELAB. Another method is based on a modified spectral model of the acquisition system. ...
... For the set of three filters, we also applied a nonlinear method, 24,[30][31][32] in which third-order 3-D polynomial regression is applied on the cubic root of the camera output ͑see the last line of Table 3͒. This method with 3 filters outperforms the linear regression with 17 filters! ...
... This confirms the results obtained for flatbed scanners in previous research. 24,[30][31][32] A possible extension to improve further the results would be to use nonlinear regression methods also with KϾ3 filters. One would need to make sure that the number of patches of the color chart remains larger than the number of parameters of the model, however. ...
In this article we describe the experimental setup of a multispectral image acquisition system consisting of a professional monochrome CCD camera and a tunable filter in which the spectral transmittance can be controlled electronically. We have performed a spectral characterisation of the acquisition system taking into account the acquisition noise. To convert the camera output signals to device-independent data, two main approaches are proposed and evaluated. One consists in applying regression methods to convert from the # camera outputs to a device-independent colour space such as CIEXYZ or CIELAB. Another method is based on a spectral model of the acquisition system. By inverting the model using a Principal Eigenvector approach, we estimate the spectral reflectance of each pixel of the imaged surface. Keywords: Liquid Crystal Tunable Filter, spectral reflectance, multispectral imaging, spectral match, metamerism 1. INTRODUCTION Already in 1853 Grassmann stated that three variable...
... We now address the problem of how to retrieve radiometric and spectrophotometric information from these camera responses. A first approach is to define a direct colorimetric transformation from the camera responses into for example the CIELAB space [37] under a given illuminant, minimising typically the root mean square error in a way similar to what is often done for conventional three-channel image acquisition devices [38]. Given an appropriate regression model, this is found to give quite satisfactory results in terms of colorimetric errors [20]. ...
Introduction In this chapter we describe a system for the acquisition of multispectral images using a CCD camera with carefully selected optical filters. We further present an application where the acquired multispectral images are used to simulate the image of a scene as it would have appeared under a given illuminant. For this application, the use of multispectral images is found to yield a much higher accuracy compared to traditional methods using only three-channel colour images. A multispectral image is an image where each pixel contains information about the spectral reflectance of the imaged scene. Multispectral images carry information about a number of spectral bands: from three components per pixel for colour images to several hundreds of bands for hyperspectral images. Multispectral imaging is relevant to several domains of application, such as remote sensing [1], astronomy [2], physics, analysis of museological objects [3, 4], cosmetics, medicine [5], high-accuracy
... The proposed characterization technique based on an empirical model provides a practical tool to transform colors between any two color spaces, for example between scanner RGB space and printer CMY. In a color management application, it is preferred to connect the device-dependent color representations to some deviceindependent color space [11][12][13][14] . We have chosen the CIELAB space 15 for this purpose since it is used extensively both in literature and industry. ...
... Using these two structures, the inner structure and the surrounding structure as defined above, we are now able to calculate, by tetrahedral interpolation, the transformation from CIELAB to CMY for any point belonging to the definition domain of CIELAB space. This is typically done either directly for all pixels of an image to be printed, or for all the vertices of a regular grid composing a CIELAB-to-CMY 3D look-up table which can be stored in a device profile and then further used by a color management system 11,13,36 . ...
A new method for the colorimetric characterization of a printer is proposed. It can also be applied to any other type of digital image reproduction device. The method is based on a computational geometry approach. It uses a 3D triangulation technique to build a tetrahedral partition of the printer color gamut volume and it generates a surrounding structure enclosing the definition domain. The characterization provides the inverse transformation from the device-independent color space CIELAB to the device-dependent color spaceCMY, taking into account both colorimetric properties of the printer, and color gamut mapping. Key words: Color printing, Device characterization, Computational geometry, Triangulation, Printer gamut, Gamut mapping, CIELAB space 1 Introduction The characterization of a color output device such as a digital color printer defines the relationship between the device color space and a device-independent color space, typically based on CIE colorimetry. This relationshi...
... We now adress the problem of how to use these camera responses. A first approach is to define a direct colorimetric transformation from the camera responses c K into for example the CIELAB space [12] under a given illuminant, minimising typically the root mean square error, in a way similar to what is often done for conventional three-channel image acquisition devices [13]. Given an appropriate regression model, this is found to give quite satisfactory results in terms of colorimetric errors [14]. ...
... We hence obtainr from r throughr = Q 1 c K = Q 1 t r = Q 1 t Ra: (11) Inserting Equations (10) and (11) into the expression r =r gives Q 1 t Ra = Ra: (12) Assuming that R is a good representation of the reflectances that will be encountered, Equation (12) should be true for any a, and hence Q 1 t R = R: (13) This gives then the reconstruction operator minimising the RMS spectral error by a pseudoinverse approach as ...
this paper we investigate the colorimetric simulation of a scene as viewed under different illuminants. Applied to fine arts paintings, museological objects, jewelry, textiles, etc., such simulations could be of particular interest as a multimedia application. It is well known that the appearance of an object or a scene may change considerably when the illuminant changes, due to physical and psychophysical effects. These effects are taken into account in most colour appearance models in a somewhat heuristic manner. However, such models can not predict correctly changes for arbitrary illuminants, one important reason for this being metamerism. To be able to predict quantitatively the physical phenomena involved in a change between any illuminants, a more complete spectral description of the illuminants and the scene reflectances is needed. The acquisition of multispectral images is thus required. A multispectral image is an image where each pixel contains information about the spectral reflectance of the imaged scene. Multispectral images carry information about a number of spectral bands: from three components per pixel for colour images to several hundreds of bands for hyperspectral images. Multispectral imaging is relevant to several domains of application, such as remote sensing [1], physics, analysis of museological objects [2], cosmetics, medicine [3], high-accuracy colour printing, or computer graphics [4]. Hyperspectral image acquisition systems are complex and expensive, limiting their current use mainly to remote sensing applications. Multispectral scanners are mostly based on a point-scan scheme [5], and are thus too slow for our applications.
... Then we analyze colorimetrically the printed chart to obtain the CIELAB values corresponding to each sample. This analysis can be done with either i) a desktop scanner properly calibrated, 8 or ii) a colorimeter, or better, iii) a spectrophotometer if available. Brettel et al. 9 propose a versatile spectrophotometer for this purpose. ...
... This is typically done either i) directly for all pixels of an image to be printed, or ii) for all the vertices of a regular grid composing a CIELABto-CMY 3D lookup-table which can be stored in a device profile and further used by a color management system (CMS). 8,15 The tetrahedron T P that encloses the input CIELAB point P is located using a 'walking' algorithm. If T P belongs to the surrounding structure, then P is an out-ofgamut point. ...
We propose a method for the colorimetric characterization of a printer which can also be applied to any other type of digital image reproduction device. The method is based on a computational geometry approach. It uses a 3D triangulation technique to build a tetrahedral partition of the printer color gamut volume and it generates a surrounding structure enclosing the definition domain. The characterization provides the inverse transformation from the deviceindependent color space CIELAB to the device-dependent color space CMY, taking into account both colorimetric properties of the printer, and color gamut mapping.
To improve the quality of scanned image captured by cameras, we propose a novel and efficient method for image processing by using color-correction and L0 gradient minimization. Our method is divided into two steps. To derive a colorimetric mapping between digital RGB signals and real image values, we use a polynomial model by considering the interrelations among the standard color spaces. A L0 gradient minimization is used to remove the image noises. Based on the half-quadratic splitting method, an iterative algorithm for our proposed method is developed. The iterative algorithm is easy to implement with the optimal complexity O(NlogN). Our method is particularly beneficial to correct image color and remove image noises. Various tests are presented to demonstrate the robustness and efficiency of our method.
