Vector Median Filter Research Articles

An Efficient Color-Impulse Detector and its Application to Color Images

By analyzing quaternion rotation theory, the color difference between two color pixels can be represented in quaternion form. Based on this representation of color difference, an efficient color-impulse detector for switching vector median filters is presented. Extensive simulations indicate that the proposed algorithm provides a significant improvement over many of the existing vector filtering techniques in suppressing impulses with different noise ratios. In particular, the proposed approach effectively preserves the fine thin lines and other detail features. The low computational complexity is also one of its advantages

An impulsive noise color image filter using learning-based color morphological operations

Morphological filter is a kind of morphological operation-based nonlinear filter. It is effective in impulsive noise filtering and has been extensively studied in the past two decades. In the presented study, a new multichannel filtering approach established on learning-based color morphological operations for impulsive noise removal in color image is presented. By using the color pixel ordering scheme learned from the pre-estimation of impulsive noise, contaminated pixels are ordered as maximum ones in erosion operation or minimum ones in dilation operation, respectively. This characteristic ensures that only uncontaminated color pixels are distributed in morphological operations, hence noisy pixels are suppressed. Reconstruction is followed to alleviate the blurring and bias effects of morphological operations and to preserve image features. The presented filtering approach greatly enhances the performance of morphological operation-based filters, especially in the color image highly corrupted by impulsive noise. Experiments and comparisons with classical filters, such as basic vector median filter (VMF), basic vector directional filter (BVDF), NOPNCP filter, etc., as well as some newly developed filters, are performed to demonstrate the effectiveness of the proposed color image filtering algorithm.

Sharpening vector median filters

A sharpening vector median (VM) filter for simultaneous denoising and enhancing vector-valued signals is introduced. This filter uses the trimmed aggregated distance minimization concept and robust vector order statistics to enhance edges and image details while retaining the noise removal characteristics of the standard VM operator. The procedure accommodates various design, implementation and application objectives by enhancing the vector-valued signals depending on the local image statistics and/or the user's needs. The filter properties discussed in this paper are proven and suggest that the proposed solution is a robust vector processing operator. The performance and efficiency of the filter are analyzed and commented upon. Examples from its application to color image filtering and virtual restoration of artworks are provided.

Algorithms for impulse noise removal from corrupted color images

Two effective algorithms for the removal of impulse noise from color images are proposed. The algorithms consist of two steps. The first algorithm detects outliers with the help of spatial relations between the components of a color image. Next, the detected noise pixels are replaced with the output of a vector median filter over a local spatially connected area excluding the outliers, while noise-free pixels are left unaltered. The second algorithm transforms a color image to the YCbCr color space that perfectly separates the intensity and color information. Then outliers are detected using spatial relations between transformed image components. The detected noise pixels are replaced with the output of a modified vector median filter over a spatially con- nected area. Simulation results in test color images show a superior performance of the proposed algorithms compared with the conventional vector median filter. The comparisons are made using the mean square error, the mean absolute error, and a subjective human visual error criterion.

Fast implementation of generalised median filter

The vector median filter (VMF) is a widely used means of eliminating impulse noise in colour images, and has the advantage of largely eliminating colour bleeding. The generalised median filter (GMF) has the capability for eliminating even more noise than the VMF, while not being targeted quite so specifically at colour bleeding. Ways of implementing the GMF are demonstrated so that it runs sufficiently rapidly to make it a useful alternative to the VMF, thereby opening the way for much wider use of this highly effective type of filter.

A switching vector median filter based on the CIELAB color space for color image restoration

Vector median filter is highly effective in removing impulsive noise from color images. However, it fails to distinguish thin lines and boundaries from impulsive noise, and usually filters them out because it interprets these fine details as some noise. This paper introduces a new solution for impulsive noise detection in color images. The proposed solution is a switching vector filter which analyzes the color difference of two pixels in the CIELAB color space using four directional operators. Based on the result of this analysis, the impulse detection module can identify noise pixels, which are replaced in the filtering module with some robust estimate. The extensive experimental results show that the proposed solution outperforms many of the existing vector filters in terms of the filtering performance. In particular, the proposed approach can effectively preserve the thin lines, fine details, and image edges.

Fast adaptive optimization of weighted vector median filters

Weighted vector median (WVM) filters are effective tools for multichannel signal processing. To obtain the desired filtering behavior and characteristic, the WVM filter weights must be determined in an appropriate manner. In this paper, we first analyze previously defined approaches for WVM filter optimization and show their drawbacks related to derivative computation and vector direction information utilization. Based on this analysis, we propose two fast adaptive algorithms for WVM filter design. Proposed Algorithm I computes locally optimal weight changes at each iteration and updates the filter weights accordingly. This algorithm does not involve derivative computation, thus eliminating the instability caused by derivative approximations utilized in previous approaches. Proposed Algorithm II extends the results from established marginal weighted median optimization methods to the vector case by error metric generalization. Both algorithms can be applied to WVM filters using the Lp norm, while Algorithm I can operate on more general distance metrics. The presented simulation results show that both algorithms are effective, fast, and stable; they perform well under a wide range of circumstances

Efficient Video Transcoding from H.263 to H.264/AVC Standard with Enhanced Rate Control

A new video coding standard H.264/AVC has been recently developed and standardized. The standard represents a number of advances in video coding technology in terms of both coding efficiency and flexibility and is expected to replace the existing standards such as H.263 and MPEG-1/2/4 in many possible applications. In this paper we investigate and present efficient syntax transcoding and downsizing transcoding methods from H.263 to H.264/AVC standard. Specifically, we propose an efficient motion vector reestimation scheme using vector median filtering and a fast intraprediction mode selection scheme based on coarse edge information obtained from integer-transform coefficients. Furthermore, an enhanced rate control method based on a quadratic model is proposed for selecting quantization parameters at the sequence and frame levels together with a new frame-layer bit allocation scheme based on the side information in the precoded video. Extensive experiments have been conducted and the results show the efficiency and effectiveness of the proposed methods.

Efficient Impulsive Noise Suppression based on Statistical Confidence Limits

Probably, the most well-known vector filter is the vector median filter (VMF) which is based on the theory of robust statistics and performs good noise suppression in color images. However, the VMF is designed to perform a fixed amount of smoothing. This may lead to too much unnecessary substitutions in the input image and, as a result, blurring and loss of image details. In order to avoid this drawback when dealing with impulsive noise, the switching schemes aim at selecting a set of pixels of the input image to be filtered leaving the rest of the pixels unchanged. In this paper, two switching filters which base the selection of the noisy pixels to be filtered on statistical tests are proposed. The proposed filters present good noise suppression while preserving fine image details appropriately. Comparisons to classical and recently introduced impulsive noise multichannel filters are provided. Moreover, the noisy pixel selection techniques are computationally simple, and the filters significantly reduce the computational complexity of the VMF.

Adaptive and vector directional processing applied to video colour images

The adapted K-nearest neighbour and adaptive vector median filters together with vector directional processing to filter 3D images are presented. Vector median, median and adaptive vector median filters have been modified and proved to suppress impulsive noise in the video sequences.

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.

Fast detection and impulsive noise removal in color images

In this paper, a novel approach to the impulsive noise removal in color images is presented. The proposed technique employs the switching scheme based on the impulse detection mechanism using the so-called peer group concept. Compared to the vector median filter and other commonly used multichannel filters, the proposed technique consistently yields very good results in suppressing both the random and fixed-valued impulsive noise. The main advantage of the proposed noise detection framework is its enormous computational speed, which enables efficient filtering of color images in real-time applications.

Wavelet Transform Based 3D Scattered Data Processing in Binocular Micro Stereovision System

In micro stereovision system, false matching due to the randomicity of image signal makes 3D reconstruction data contain a large amount of abnormal data in the form of noise (pulse noise, Gauss noise). In order to obtain a more accurate 3D reconstruction shape, a novel method of 3D scattered data processing, which is a combination of wavelet transform and extended Vector Median Filters, is proposed to use wavelet transform to realize primary denosing, and use extended Vector Median Filters to further denoise thereby achieving higher precision 3D measurement. In wavelet based denosing, wavelet based edge detector in vector space removes edge abnormal points effectively, moreover, boundary tracking is used to prune out the image background from the matching area to remove exterior abnormal points. Then the local areas determined by edges are filtered by extended vector median filter. Experimental results indicate that the method proposed is effective to removing abnormal data from the 3D reconstructed data and enables 3D measurement position accuracy to reach 3 m µ .

A Statistically-Switched Adaptive Vector Median Filter

This paper presents a new cost-effective, adaptive multichannel filter taking advantage of switching schemes, robust order-statistic theory and approximation of the multivariate dispersion. Introducing the statistical control of the switching between the vector median and the identity operation, the developed filter enhances the detail-preserving capability of the standard vector median filter. The analysis and experimental results reported in this paper indicate that the proposed method is capable of detecting and removing impulsive noise in multichannel images. At the same time, the method is computationally efficient and provides excellent balance between the noise attenuation and signal-detail preservation. Excellent performance of the proposed method is tested using standard test color images as well as real images related to emerging virtual restoration of artworks.

Generalized Selection Weighted Vector Filters

This paper introduces a class of nonlinear multichannel filters capable of removing impulsive noise in color images. The here-proposed generalized selection weighted vector filter class constitutes a powerful filtering framework for multichannel signal processing. Previously defined multichannel filters such as vector median filter, basic vector directional filter, directional-distance filter, weighted vector median filters, and weighted vector directional filters are treated from a global viewpoint using the proposed framework. Robust order-statistic concepts and increased degree of freedom in filter design make the proposed method attractive for a variety of applications. Introduced multichannel sigmoidal adaptation of the filter parameters and its modifications allow to accommodate the filter parameters to varying signal and noise statistics. Simulation studies reported in this paper indicate that the proposed filter class is computationally attractive, yields excellent performance, and is able to preserve fine details and color information while efficiently suppressing impulsive noise. This paper is an extended version of the paper by Lukac et al. presented at the 2003 IEEE-EURASIP Workshop on Nonlinear Signal and Image Processing (NSIP '03) in Grado, Italy.

Fast motion re-estimation for arbitrary downsizing video transcoding using H.264/AVC standard

Downsizing video transcoding is a technique for adapting the bit rate or frame size of a preceded video to suit better the constraints of transmission networks and target applications. To sustain good video quality at reduced bit rates, we have previously proposed H.263-based transcoding techniques for downsizing preceded videos by an arbitrary factor. To fully exploit the advantages of video transcoding, we investigate and propose in this paper an effective and fast motion re-estimation method for arbitrary downsizing video transcoding using the newly developed H.264/AVC standard. Specifically, as H.264/AVC allows more macroblock partitions and reference frames for inter-frame motion-compensated prediction, we propose an area-weighted vector median filter to estimate the new required motion vectors from the motion vectors existing in a preceded video. Experimental results show that the proposed method can achieve a notable improvement in both subjective and objective video quality for transcoding preceded H.263 or H.264 videos at reduced bit rates and frame sizes.

Fuzzy vector median-based surface smoothing

This paper proposes a novel approach for smoothing surfaces represented by triangular meshes. The proposed method is a two-step procedure: surface normal smoothing through fuzzy vector median (FVM) filtering followed by integration of surface normals for vertex position update based on the least square error (LSE) criteria. Median and Order Statistic-based filters are extensively used in signal processing, especially image processing, due to their ability to reject outliers and preserve features such as edges and monotonic regions. More recently, fuzzy ordering theory has been introduced to allow averaging among similarly valued samples. Fuzzy ordering theory leads naturally to the fuzzy median, which yields improved noise smoothing over traditional crisp median filters. This paper extends the fuzzy ordering concept to vector-based data and introduces the fuzzy vector median filter. The application of FVM filters to surface normal smoothing yields improved results over previously introduced normal smoothing algorithms. The improved filtering results, coupled with LSE vertex position update, produces surface smoothing that minimizes the effects of noise while simultaneously preserving detail features. The proposed method is simple to implement and relatively fast. Simulation results are presented showing the performance of the proposed method and its advantages over commonly used surface smoothing algorithms. Additionally, optimization procedures for FVM filters are derived and evaluated.

An improved motion-compensated restoration method for damaged color motion picture films

In this paper, we propose an improved motion-compensated restoration method for color motion picture films deteriorated due to flashing blotches. The method consists of an improved multiresolution block matching with log-D search, a rank ordered differences-based blotch detection and 3D vector median filtering for interpolation of missing data, and utilizes five consecutive frames. Performance of the method is tested on artificially corrupted image sequences and real motion picture films, and is compared to that of the three-frame-based method which involves similar algorithms except improved motion estimation and blotch detection. The results show that the method efficiently works even in severely blotched and motion regions in image sequences.

Observations on Adaptive Vector Filters for Noise Reduction in Color Images

In a series of papers, Plataniotis et al. proposed a number of filters for noise reduction in color images where the noise type is unknown. In this letter, those filters with a unified notation are summarized, and it is shown that they are essentially variants of the same filtering procedure. It is also shown that the class of adaptive vector filters can be considered as interpolants between the arithmetic mean filter and the vector median filter. Results are presented of numerical computations with the filters on test images corrupted with noise. It is found that the adaptive vector filters perform well with general applicability.

New filtering technique for the impulsive noise reduction in color images

We present a novel approach to the problem of impulsive noise reduction for colorimages. The new image‐filtering technique is based on the maximization of the similarities between pixels in the filtering window. Themethod is able to remove the noise component, while adapting itself to the local image structure. In this way, the proposed algorithm eliminates impulsive noise while preserving edges and fine image details. Since the algorithm can be considered as a modification of the vector median filter driven by fuzzy membership functions, it is fast, computationally efficient, and easy to implement. Experimental results indicate that the new method is superior, in terms of performance, to algorithms commonly used for impulsive noise reduction.