Weighted Vector Median Filter Research Articles

An effective weighted vector median filter for impulse noise reduction based on minimizing the degree of aggregation

An effective weighted vector median filter for impulse noise reduction based on minimizing the degree of aggregation

Triple-Frame-Based Bi-Directional Motion Estimation for Motion-Compensated Frame Interpolation

In this paper, new motion estimation (ME) and motion vector refinement (MVR) methods for motion-compensated frame interpolation (MCFI) are proposed. To estimate motion vectors (MVs) with great reliability, triple-frame-based bi-directional ME (TFBME), which employs three sequential frames, is developed. In TFBME, the middle frame works as a reference to estimate MVs between two end frames, which leads to performance improvement especially when objects move linearly through three sequential frames. In addition, a modified bi-directional ME method, which employs only two successive frames, is combined with TFBME to handle cases in which objects move non-linearly through three sequential frames. In our proposed MVR, artifacts on interpolated frames are first detected by a convolutional neural network and then corrected by weighted vector median filtering. Experimental results show that the proposed MCFI method outperforms conventional methods in both subjective and objective evaluations.

A two-stage switching vector median filter based on quaternion for removing impulse noise in color images

This paper presents a novel two-stage filtering algorithm for removing impulse noise in color images. Quaternion theory is used to represent the intensity and chromaticity differences of two color pixels. Use of quaternion treats color pixels as vectors and processes color images as single unit rather than as separated color components. This preserves the existing correlation and three dimensional vector natures of the color channels. In the first stage of noise detection, the color pixels are sorted and assigned a rank based on the aggregated sum of color pixel differences with other pixels inside the filtering window. The central pixel is considered as probably corrupted by an impulse if its rank is bigger than a predefined rank. In the second stage, the probably corrupted candidate is again checked for an edge or an impulse by using four Laplacian convolution kernels. If the minimum difference of these four convolution is larger than a predefined threshold, then the central pixel is regarded as an impulse. For filtering, we extend the size of the sliding window to cover more pixels information. The noisy pixel is replaced by output of weighted vector median filter implemented using the quaternion distance. More weight is assigned to those pixels belonging to the direction of minimum difference. Experimental results indicate the improved performance of the proposed filter in suppressing the impulse noise while retaining the original image details comparing against other well-known filters.

An effective vector filter for impulse noise reduction based on adaptive quaternion color distance mechanism

Abstract The measure of color distances plays an important role in color image processing. An effective color distance method is based on quaternion representation, which computes a color distance by weighted average of the distances of luminance and quaternion chromaticity. However, the mechanism of assigning fixed weights to luminance and chromaticity distances cannot always effectively measure color distances, since in a color image chromaticity can change significantly. To address this issue, this paper proposes an adaptive weighted quaternion color distance method. Based on the new color distance measure, the robust outlyingness ratio and local reachability density, which are defined in grayscale images, are extended to color images to implement a coarse-to-fine color noise detection operator. In noise filtering, a weighted vector median filter is employed to restore the pixels judged as noisy. Experimental results exhibit the validity of the proposed method by showing better performance in terms of both objective criteria and visual effect, compared to other widely-used color image filtering methods.

Quaternion Switching Vector Median Filter Based on Local Reachability Density

Impulse noise detection is important to the restoration of color images contaminated by impulse noise in switching vector median filters. To increase detection accuracy, an effective color-impulse detector is presented. A new color distance metric based on quaternion theory is proposed. The proposed color distance metric is used to calculate the local density of a color pixel. A hard thresholding strategy is used to determine whether a color pixel is corrupted by impulse noise or not (i.e., an outlier). The noisy pixels detected will be restored by a weighted vector median filter, while the noise-free pixels remain unchanged. The experimental comparisons show that the proposed algorithm can obtain lower false and miss detection rate, and produces better performance in terms of peak signal-to-noise ratio and feature similarity measures, compared to other well-known color image filtering methods.

An adaptive switching filter based on approximated variance for detection of impulse noise from color images.

A new adaptive switching algorithm is presented where two adaptive filters are switched correspondingly for lower and higher noise ratio of the image. An adaptive center weighted vector median filter is used for the lower noise ratio whereas for higher noise ratio the noisy pixels are detected based on the comparison of the difference between the mean of the vector pixels in the window and the approximated variance of the vector pixels in the window. Then the window comprising the detected noisy pixel is further considered where the pixels are given exponential weights according to their similarity to the other neighboring pixels, spatially and radio metrically. The noisy pixels are then replaced by the weighted average of the pixels within the window. The filter is able to preserve higher signal content in the higher noise ratio as compared to other robust filters in comparison. With a little high in computational complexity, this technique performs well both in lower and higher noise ratios. Simulation results on various RGB images show that the proposed algorithm outperforms many other existing nonlinear filters in terms of preservation of edges and fine details.

Two-stage quaternion switching vector filter for color impulse noise removal

In this paper, a new approach to impulse noise removal in color images is presented. The proposed solution is a quaternion switching vector filter in which the impulse detection consists of two stages. First, by using quaternion representation, an effective color distance measure method is developed. Then, based on the new color distance measure, the proposed filter utilizes the directional samples along four directions to classify image pixels into possible noisy and noise-free ones. For possible noisy pixels, the concept of peer group is modified and extended to the directional samples to further detect whether they are corrupted by impulse noise or not. Finally, a weighted vector median filter is performed only on the pixels that are identified as noisy by the second stage. The experimental comparisons exhibit the validity of the proposed solution by showing significant performance improvements over other well-known color image filtering methods.

Tracking Pedestrians based on Patch Matching

One of the challenging issues in machine vision over the last decade involves how to track people in consecutive video frames. Nowadays, scholars are greatly interested in tracking people in videos due to a need to track moving objects. In this paper we track pedestrians through the Dollár’s detection technique combined with patch matching from detected objects. At this stage, the patches are matched separately through Bhattacharyya’s method in order to increase the tracking precision. We use color histogram with background subtraction. Desirable results are obtained in estimating displacement and patch search space through the combination of displacement vectors with the detection results from weighted vector median filter (WVMF). Our new technique indicates that we were able to track pedestrians more accuracy.

Color Image Enhancement Based on Ant Colony Optimization Algorithm

In the collection, transmission, decoding process, the images are likely to produce noise. Noise makes the image color distorted and the articulation dropped, and also affects the image quality. Due to different causes, there are different types of noise, and the impulse noise is most common among them which exert great influence on the image quality. This paper, according to the characteristics of the color image, combines the ant colony algorithm and weighted vector median filter method to put forward an algorithm for the impulse noise removal and the color image enhancement. This method finds the optimal filter bank parameter by ant colony optimization (ACO) and processes image points polluted by the noise to achieve the purpose of image enhancement and protect the image details and edge information. Simulation experiment proves the correctness and validity of this method.

A switching weighted vector median filter based on edge detection

An impulse noise removal method based on noise detection and image edge detection is proposed to improve the performance of vector median filter. Corrupted pixels are first discriminated from the noise-free pixels by comparing the current pixel with the corresponding pixel in a reference image. Then corrupted pixels are filtered by the proposed weighted vector median filter. The novelty of this filter lies in its weighting technique based on image edge detection. The weight of each pixel is determined by its group which is related to the image edge. This method can suppress noise and reduce edge blurring effectively. Experimental results show that the proposed method outperforms all algorithms examined in this paper in terms of MAE, MSE and PSNR values.

Combining patch matching and detection for robust pedestrian tracking in monocular calibrated cameras

This paper presents a new approach for tracking multiple people in monocular calibrated cameras combining patch matching and pedestrian detection. Initially, background removal and pedestrian detection are used in conjunction with the vertical standing hypothesis to initialize the targets with multiples patches. In the tracking step, each patch related to a given target is matched individually across frames, and their translation vectors are combined robustly with pedestrian detection results in the world coordinate frame using weighted vector median filters. Additionally, the algorithm uses the camera parameters to both estimate the person scale in a straightforward manner and to limit the search region used to track each fragment. Our experimental results indicate that our tracker can deal with occlusions and video sequences with strong appearance variations, presenting results comparable to or better than existing state-of-the-art algorithms.

Quaternion-Based Impulse Noise Removal From Color Video Sequences

In this paper, a new quaternion vector filter for removal of random impulse noise in color video sequences is presented. First, luminance distances and chromaticity differences that are represented in quaternion form are combined together to measure color distances between color pixels. Then, based on this new color distance mechanism, the samples along horizontal, vertical, and diagonal directions in current frame and the samples of adjacent frames on motion trajectory are used to detect whether each pixel is noisy or not. By analyzing the spatiotemporal order-statistic information about these directional samples, the video pixels are classified into noise free and noisy. Finally, 3-D weighted vector median filtering is performed on the pixels that are judged as noisy, and the other pixels remain unchanged. The experimental results show that the proposed algorithm significantly outperforms other state-of-the-art video denoising methods in terms of both objective measure and visual evaluation.

Target Tracking Using Multiple Patches and Weighted Vector Median Filters

This paper presents an approach for object tracking based on multiple disjoint patches. Initially, the target is subdivided into a set of rectangular patches, and each patch is represented parametrically by the mean vector and covariance matrix computed from a set of feature vectors that represent each pixel of the target. Each patch is tracked independently based on the Bhattacharyya distance, and the displacement of the whole template is obtained using a Weighted Vector Median Filter (WVMF), which reduces the influence of incoherently tracked patches. To smooth the obtained trajectory and also cope with short-term total occlusions, a predicted displacement vector based on the motion of the target in the previous frames is also used, and an updating scheme is applied to deal with appearance changes of the template. Experimental results indicate that the proposed scheme is robust to partial and short-time total occlusions, presenting a good compromise between accuracy and execution time when compared to other approaches.

Fuzzy-Based Motion Vector Smoothing for Motion Compensated Frame Interpolation

SUMMARY We proposed a new motion vector (MV) smoothing usingfuzzy weighting and vector median filtering for frame rate up-conversion.A fuzzy reasoning system adjusts the weighting values based on the localcharacteristics of MV field including block difference and block boundarydistortion. The fuzzy weighting removes the affect of outliers and irregularMVs from the MV smoothing process. The simulation results show that theproposed algorithm can efficiently correct wrong MVs and thus improvevisual quality of the interpolated frames better than conventional methods. key words: fuzzy reasoning, motion vector smoothing, motion compen-sated frame interpolation, motion estimation 1. Introduction In the video processing, motion information described bymotion vectors (MVs) is one of the most important factorsfor many video coding applications such as object tracking,video concealment, or motion compensated frame interpo-lation. Unfortunately, tracking real motion is an ill-posedproblem. The motion estimation may be failed wheneverthere are multiple local minima in the sum of absolute dif-ferent distribution due to deformable motion, noise, objectocclusion, lighting variation, and so on. As a result, the in-terpolated frame using wrong motion vectors suffers blockartifact or ghost effect.Many motion vector processing schemes have beenproposed to derive true motion from the estimated MVs.It is possible to use the assumption that the MV fields aresmooth function. The simplest method for MV smoothingis vector median filter [1] which can simply remove MV out-liers and refine MVs from their neighborhood. Alparone etal. proposed an adaptively weighted vector median filter us-ing the measure of displaced frame difference (DFD) to ob-tain smoother MVs with true spatial correlation of the MVfield [2]. Dane et al. presented a method adopting medianand low pass filtering with MV classification based on anglevariance [3]. In the work [4], Ai-Mei Huang et al. proposedbidirectional MV processing by selecting the best MV basedon the minimum difference between forward and backwardpredictions. All these methods try to evaluate the reliabilityof MVs before applying smoothing function. However, therelation between the reliability of MVs and frame difference

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

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.

Adaptive and global optimization methods for weighted vector median filters

Abstract Weighted vector median filters (WVMF) are a powerful tool for the non-linear processing of multi-components signals. These filters are parametrized by a set of N weights and, in this paper, we propose two optimization techniques of these weights for colour image processing. The first one is an adaptive optimization of the N−1 peripheral weights of the filter mask. The major and more difficult task is to get a mathematical expression for the derivative of the WVMF output with respect to its weights; two approximations are proposed to measure this filter output sensitivity. The second optimization technique corresponds to a global optimization of the central weight alone, the value of which is determined, in a noise reduction context, by an analytical expression depending upon the mask size and the probability of occurrence of an impulsive noise. Both approaches are evaluated by simulations related to the denoising of textured, or natural, colour images, in the presence of impulsive noise. Furthermore, as they are complementary, they are also tested when used together.

Vector median-rational hybrid filters for multichannel image processing

In this letter, a new class of nonlinear filters called vector median-rational hybrid filters (VMRHFs) for multispectral image processing is introduced and applied to the color image filtering problem. These filters are based on rational functions (RFs) 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.