Color Histogram Equalization using Probability Smoothing

Publication in the conference proceedings of EUSIPCO, Florence, Italy, 200

Color display for multiwavelength astronomical images

This paper proposes a new approach for the color display of multispectral/hyperspectral images. The color representation of such data becomes problematic when the number of bands is higher than three, i.e. the basic RGB (Red, Green, Blue) representation is not straightforward. Here we employ a technique that uses a segmentation map, like an a priori information, and then compute a Factorial Discriminant Analysis (Fischer analysis) in order to allow, at best, a distribution of the information in the color space HSV (Hue, Saturation, Value). The information collected from the segmentation map (where each pixel is associated with class) has been shown to be advantages in the representation of the images through the results obtained on increasing size image collections in the framework of astronomical images. This method can easily be applied to other domains such as polarimetric or remote sensing imagery.Cet article propose une nouvelle méthode de représentation et de visualisation en couleur d'images multispectrales ou hyperspectrales. Le problème de la visualisation de telles données est en effet problématique dès que le nombre de bandes spectrales est supérieur à trois, i.e., la représentation triviale RVB (Rouge, Vert, Bleu) n'est plus directe. Le principe consiste ici à utiliser une carte de segmentation préalablement obtenue, a priori, et à réaliser une analyse factorielle discriminante permettant de distribuer au mieux l'information dans l'espace des couleurs TSL (Teinte, Saturation, Luminance). L'information apportée par la carte de segmentation (chaque site est associé à une classe) peut se révéler judicieuse comme le montrent les résultats obtenus sur des lots d'images de tailles croissantes dans le cadre de l'imagerie astronomique. Cette méthode est générale et s'applique également à d'autres domaines manipulant des images multicomposantes ou multivariées comme en télédétection ou en imagerie polarimétrique

Color histogram equalization through mesh deformation

©2003 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or distribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE. This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder.Presented at ICIP-2003 : 2003 International Conference on Image Processing, September 14-17, 2003, Barcelona, Spain.In this paper we propose an extension of grayscale histogram equalization for color images. For aesthetic reasons, previously proposed color histogram equalization techniques do not generate uniform color histograms. Our method will always generate an almost uniform color histogram thus making an optimal use of the color space. This is particularly interesting for pseudo-color scientific visualization. The method is based on deforming a mesh in color space to fit the existing histogram and then map it to a uniform histogram. It is a natural extension of grayscale histogram equalization and it can be applied to spatial and color space of any dimension

Color Histogram Equalization Through Mesh Deformation

In this paper we propose an extension of grayscale histogram equalization for color images. For aesthetic reasons, previously proposed color histogram equalization techniques do not generate uniform color histograms. Our method will always generate an almost uniform color histogram thus making an optimal use of the color space. This is particularly interesting for pseudo-color scientific visualization. The method is based on deforming a mesh in color space to fit the existing histogram and then map it to a uniform histogram. It is a natural extension of grayscale histogram equalization and it can be applied to spatial and color space of any dimension