Distance Measures for Reduced Ordering Based Vector Filters

Reduced ordering based vector filters have proved successful in removing long-tailed noise from color images while preserving edges and fine image details. These filters commonly utilize variants of the Minkowski distance to order the color vectors with the aim of distinguishing between noisy and noise-free vectors. In this paper, we review various alternative distance measures and evaluate their performance on a large and diverse set of images using several effectiveness and efficiency criteria. The results demonstrate that there are in fact strong alternatives to the popular Minkowski metrics

Performance of Basic Vector Directional Filters According to Used Angle Distance

Color images are typical examples of vector-valued signals. For that reason, vector processing represents an optimal approach. Although widely used vector filter is a vector median based on a reduced ordering, the directional processing with utilizing of the angle between input vectors can be used, too. By this way can be achieved well estimates, since vector directional filters preserve color chromaticity, whereas vector median filters may not satisfy this requirement. So, this paper is focused on the performance of basic vector directional filter in dependence on the various angle distances

GPU Accelerated Vector Median Filter

Noise reduction is an important step for most image processing tasks. For three channel color images, a widely used technique is vector median filter in which color values of pixels are treated as 3-component vectors. Vector median filters are computationally expensive; for a window size of n x n, each of the n(sup 2) vectors has to be compared with other n(sup 2) - 1 vectors in distances. General purpose computation on graphics processing units (GPUs) is the paradigm of utilizing high-performance many-core GPU architectures for computation tasks that are normally handled by CPUs. In this work. NVIDIA's Compute Unified Device Architecture (CUDA) paradigm is used to accelerate vector median filtering. which has to the best of our knowledge never been done before. The performance of GPU accelerated vector median filter is compared to that of the CPU and MPI-based versions for different image and window sizes, Initial findings of the study showed 100x improvement of performance of vector median filter implementation on GPUs over CPU implementations and further speed-up is expected after more extensive optimizations of the GPU algorithm

Le vecteur de meilleur rang moyen : une statistique pour l'analyse de données multidimensionnelles -Application au filtrage d'images couleurs

L'obtention du meilleur représentant d'un échantillon est une question ouverte et dépend de la statistique utilisée. Cette communication propose une statistique, qui repose sur le calcul des rangs, et qui permet en outre d'introduire la notion de données représentant le mieux son échantillon. La donnée représentant le mieux son échantillon est nommée donnée de meilleur rang moyen. En appliquant notre statistique au filtrage d'images couleurs, nous illustrons l'efficacité et l'intérêt de notre procédure, visuellement et numériquement

A new method to calculate mathematical morphology using associative memory and cellular learning automata

Abstract: The methods presented in this paper include using auto-associative memory which can be defined as a supervised organizing method which is a specific type of h-sorting which is compatible with the morphologic operators over multi-variables data. Mathematical morphologies for multi-variable images require appropriate sort descriptions that allow us to define and use primitive morphologies operators without any wrong results such as wrong color. All the required calculations are defined with lattice algebra (+,^ and ∨); therefore, the proposed method will be faster with less computation overhead than the previous methods. This method does not use any assumptions of the stochastic process which means that this method is independent of the model. The presented method uses cellular learning automata which results in fewer errors than the mathematical methods due to the feedback from the network