The goal of the work reported in this dissertation is to develop methods for the acquisition and reproduction of high quality digital color images. To reach this goal it is necessary to understand and control the way in which the different devices involved in the entire color imaging chain treat colors. Therefore we addressed the problem of colorimetric characterization of scanners and printers, providing efficient and colorimetrically accurate means of conversion between a device-independent color space such as the CIELAB space, and the device-dependent color spaces of a scanner and a printer.
First, we propose a new method for the colorimetric characterization of color scanners. It consists of applying a non-linear correction to the scanner RGB values followed by a 3rd order 3D polynomial regression function directly to CIELAB space. This method gives very good results in terms of residual color differences. The method has been successfully applied to several color image acquisition devices, including digital cameras. Together with other proposed algorithms for image quality enhancements it has allowed us to obtain very high quality digital color images of fine art paintings.
An original method for the colorimetric characterization of a printer is then proposed. 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 space CMY, taking into account both colorimetric properties of the printer, and color gamut mapping.
To further improve the color precision and color fidelity we have per-formed another study concerning the acquisition of multispectral images using a monochrome digital camera together with a set of K >3 carefully selected color filters. Several important issues are addressed in this study. A first step is to perform a spectral characterization of the image acquisition system to establish the spectral model. The choice of color chart for this characterization is found to be very important, and a new method for the design of an optimized color chart is proposed. Several methods for an optimized selection of color filters are then proposed, based on the spectral properties of the camera, the illuminant, and a set of color patches representative for the given application. To convert the camera output signals to device-independent data, several approaches are proposed and tested. One consists of applying regression methods to convert to a color space such as CIEXYZ or CIELAB. Another method is based on the spectral model of the acquisition system. By inverting the model, we can estimate the spectral reflectance of each pixel of the imaged surface. Finally we present an application where the acquired multispectral images are used to predict changes in color due to changes in the viewing illuminant. This method of illuminant simulation is found to be very accurate, and it works well on a wide range of illuminants having very different spectral properties. The proposed methods are evaluated by their theoretical properties, by simulations, and by experiments with a multispectral image acquisition system assembled using a CCD camera and a tunable filter in which the spectral transmittance can be controlled electronically.