Efficient Architecture and Implementation of Vector Median Filter in Co-Design Context

This work presents an efficient fast parallel architecture of the Vector Median Filter (VMF) using combined hardware/software (HW/SW) implementation. The hardware part of the system is implemented using VHDL language, whereas the software part is developed using C/C++ language. The software part of the embedded system uses the NIOS-II softcore processor and the operating system used is μClinux. The comparison between the software and HW/SW solutions shows that adding a hardware part in the design attempts to speed up the filtering process compared to the software solution. This efficient embedded system implementation can perform well in several image processing applications

Vector Directional Distance Rational Hybrid Filters for Color Image Restoration

A new class of nonlinear filters, called vector-directional distance rational hybrid filters (VDDRHF) for multispectral image processing, is introduced and applied to color image-filtering problems. These filters are based on rational functions (RF). The VDDRHF filter is a two-stage filter, which exploits the features of the vector directional distance filter (VDDF), the center weighted vector directional distance filter (CWVDDF) and those of the rational operator. The filter output is a result of vector rational function (VRF) operating on the output of three sub-functions. Two vector directional distance (VDDF) filters and one center weighted vector directional distance filter (CWVDDF) are proposed to be used in the first stage due to their desirable properties, such as, noise attenuation, chromaticity retention, and edges and details preservation. Experimental results show that the new VDDRHF outperforms a number of widely known nonlinear filters for multi-spectral image processing such as the vector median filter (VMF), the generalized vector directional filters (GVDF) and distance directional filters (DDF) with respect to all criteria used.

Adaptive fuzzy order statistics-rational hybrid filters for color image processing

In this paper. multichannel image processing using an adaptive approach is studied. The proposed approach is simpler and more appropriate than the traditional approaches that have been addressed by means of groupwise vector ordering information. These adaptive techniques are formed by a two-layer filter based on rational functions using fuzzy transformations of either the Euclidean or angular distances among the different vectors to adapt to local data in the color image, The output is the result of a vector rational operation taking into account three fuzzy sub-fraction outputs. Extensive simulation results illustrate that the new, adaptive Fuzzy filters are computationally attractive and achieve noise attenuation, chromaticity retention, and edges and details preservation. (C) 2002 Elsevier Science B.V. All rights reserved

Vector median-rational hybrid filters for multichannel image processing

In this letter, a new class of nonlinear filters called vector median-rational hybrid filters (VMRHF's) for multispectral image processing is introduced and applied to the color image filtering problem. These filters are based on rational functions (RF's) offering a number of advantages. First, a rational function is a universal approximator and a good extrapolator. Second, it can be trained by a linear adaptive algorithm. Third, it produces the best approximation (w.r.t. a given cost function) for some specific functions. The output is the result of a vector rational operation over the output of three subfilters, such as two vector median (VM) subfilters and one center weighted vector median filter (CWVMF). These filters exhibit desirable properties, such as edge and details preservation and accurate chromaticity estimation

Generalised class of nonlinear-type hybrid filters

A new class of nonlinear filters called the generalised directional distance rational hybrid filters (GDDRHF) for multidimensional signal processing is described. The application at hand is colour image filtering problems. The GDDRHF filter is a two-stage filter, which exploits the features of a new directional distance filter, where the temporal information related to the central sample is expressed by its weight, and those of the rational operator. This weight is incorporated to a sum of vector distances and vector angles. It is shown that the new GDDRHF outperforms a number of widely known nonlinear filters with respect to all criteria used

Median-rational hybrid filters for image restoration

A new class of nonlinear filters is introduced, called median-rational hybrid filters (MRHFs), based on rational functions (RFs). The output is the result of rational operation taking into account three sub-functions. It is shown that every sub-function will preserve details within its sub-windows. The proposed MRHF filters have the inherent property that on smooth areas they provide good noise attenuation whereas on changing areas the noise attenuation is traded for good response to change. It is shown that a consistent reduction in the objectively measured mean absolute error and mean square error is obtained