We review the phenomena having an impact on the interaction between light, paper and color halftones. They comprise surface reflections and refractions at the air-paper interface, propagation of light within the paper, internal reflections at the paper-air interface, as well as ink spreading and trapping. We introduce basic notions from radiometry and optics, such as the definitions of radiant flux, irradiance, radiance, Lambert reflector, as well as collimated and diffuse light reflection and refraction. We then present classical spectral reflection models, their extension to multi-ink color halftones and the impact of different measuring devices (radiant detector, integrated sphere). Finally we briefly review ink spreading models and highlight the topics deserving further research.

Scale space filtering is a well known approach in image segmentation. This approach determines precisely the thresholds in order to produce the image segmentation by histogram multi-thresholding. The main drawback of this approach is that it produces a coarse segmentation because it does not take into account the spatial arrangement of the pixels in the image and so, requires a second stage in order to obtain a finer segmentation. In this paper, we propose to associate a new criterion, the compactness degree, with the scale space filter in order to produce the segmentation by means of one single stage. For this purpose, this criterion exploits the connectedness and the homogeneity properties of pixels.

This paper addresses the problem of robust filtering in color images. We propose to improve the bilateral filter by using Ordered Weighted Averaging (OWA) filters. Bilateral filter decreases amount of noise preserving edges when filtering. But the results could be strongly affected by the presence of outliers. In this paper, we define the filtering in the framework of fuzzy logic. If filtering is considered as a weighted averaging, then each filter is associated with a fuzzy set. The membership values of the fuzzy set are the weights of average. In this context, the bilateral filter is a conjunction of two fuzzy sets (i.e. aggregation with AND operator): one in the spatial domain and the other one in a photometric domain. Applied to color images, we propose to extend the conjunction to three fuzzy sets: one in the spatial domain, one in the brightness domain and one in the chromatic domain. Taking into account the robustness of rank filters, we propose to define an OWA filter in order to obtain robust adaptive filters in brightness and chromaticity. The application to color images states the filtering ability for removing noise in presence of outliers.

We introduce a new method for color image sharpening based on S-CIELAB extension. S-CIELAB involves a series of smoothing spatial filters in the opponent color space to approximate the contrast sensitivity functions of the human vision system. The filters are linear combinations of Gaussian masks. We combine these spatial filters with the Laplacian operator in each opponent channel to obtain the sharpened image. The Laplacian of the smoothed components can be simplified by introducing the Laplacian of Gaussian (LoG) operator. Alternatively, the LoG operator can be approximated by the difference of Gaussians (DoG) operator. Moreover, the use of DoG operator can be justified since it is used to model the receptive field performance in early human vision. The resulting image is subtracted from the image in each opponent channel and then back transformed to the device independent representation space (XYZ) to obtain the final sharpened image.The method is tested and applied to digital color images. The results are compared with other results obtained by applying the LoG operator to the intensity channel only (keeping the chromatic components unchanged), or by applying the simple Laplacian to the image components in two representations (opponent color space and RGB).

Nowadays, the most important engine to provide confidentiality is encryption. Therefore, the classical and modern ciphers are not suitable for huge quantity of data in real-time environment. Selective encryption (SE) is an approach to encode only the most important portion of the data in order to provide a proportional privacy and to reduce computational requirements. The objective of our work is to leave free the low-resolution image and give full-resolution access only for authorized person. This approach is based on AES stream ciphering using VLC (Variable Length Coding) of the Huffman's vector. The proposed scheme allows decryption of a specific region of image and result in a significant reduction in encrypting and decrypting processing time. It also provides a constant bit rate and keep the JPEG bit-stream compliance. We have illustrated our method on a digital painting of the Louvre Museum of Paris, France.

This study investigates the validation of a tooth imaging system in assessing changes in tooth whiteness in clinical dental trials. Sixteen male and female subjects aged between 18-70 years participated in the study and were divided into two groups. The control group was given a non-whitening toothpaste, whereas the test group used a tooth-whitening system for 14 days. Five Images were taken at the beginning of the trial (baseline), 4^th, 7^th, 11^th and 14^th day of the treatment. Camera RGB values were transformed to CIELAB space, and colour differences and three whiteness indices were calculated. The test group showed obvious changes in colour coordinates and whiteness indices over the two week period, while the control group kept stable in tooth colour. At the meantime of image capture, visual assessments by a dentist were done in a typical clinical surgery. The comparison result between the camera measurements and visual assessments also proved the validation of the tooth-imaging system for measuring tooth-whiteness.

The information preserved in identifying surfaces solely by their color can be quantified by measures defined by Shannon, including capacity and mutual information. The aim of this study was (1) to determine whether the capacity of an additive Gaussian channel provides an asymptotic upper bound to mutual information estimated for natural scenes under different illuminants, and (2) to explore the effect of different color representations on mutual-information estimates.

We have previously shown that it is possible to construct a coordinate system in the space of illumination spectra such that the coordinate vectors of the illuminants are located in a cone. Changes in the space of illuminants can then be described by an intensity related scaling and a transformation in the Lorentz group SU(1,1).In practice it is often difficult and expensive to measure these coordinate vectors. Therefore it is of interest to estimate the characteristics of an illuminant from an RGB image captured by a camera. In this paper we will investigate the relation between sequences of illuminants and statistics computed from RGB images of scenes illuminated by these illuminants.As a typical example we will study sequences of black body radiators of varying temperature. We have shown earlier that black body radiators in the mired parametrization can be described by one-parameter groups of the Lorentz group SU(1,1). In this paper we will show that this group theoretical structure of the illuminant space induces a similar structure in spaces of statistical descriptors of the resulting RGB images. We show this relation for mean vectors of RGB images, for RGB histograms and for histograms of images obtained by applying certain spatiospectral linear filters to the RGB images. As a result we obtain estimates of the color temperature of the illuminant from sequences of RGB images of scenes under these illuminants.

We introduce a spatial color image retrieval method which does not include any segmentation step. This method relies on small image thumbnails and the Earth Mover's Distance (EMD). We then derive an unsupervised matching criterion using an a contrario approach. Experiments are performed on a database of illuminated manuscripts.

The Local Binary Pattern (LBP) operator computes a local texture measure that is invariant to monotonic transformations of the image grey-scale. As a result of this property, calculating the LBP value in each channel of a colour image results in a triplet of values that are invariant to changes in illumination colour. Previous research has shown that histograms of grey-scale LBP values, and histograms of LBP values calculated in the R, G and B channels independently, form useful feature vectors for image retrieval. In this paper we generate 3D histograms of LBP values for colour images. Our experiments demonstrate that image retrieval performance, on a database of objects viewed under different illuminants, is greatly improved when using the 3D histogram compared to when the histogram is calculated for each colour channel independently. Furthermore, we find that this method can give better performance than using colour-histogram based features.