Center Weighted Median Research Articles

Intelligent Control Techniques for the Detection of Biomedical Ear Infections.

The capacity to carry out one's regular tasks is affected to varying degrees by hearing difficulties. Poorer understanding, slower learning, and an overall reduction in efficiency in academic endeavours are just a few of the negative impacts of hearing impairments on children's performance, which may range from mild to severe. A significant factor in determining whether or not there will be a decrease in performance is the kind and source of impairment. Research has shown that the Artificial Neural Network technique is capable of modelling both linear and nonlinear solution surfaces in a trustworthy way, as demonstrated in previous studies. To improve the precision with which hearing impairment challenges are diagnosed, a neural network backpropagation approach has been developed with the purpose of fine-tuning the diagnostic process. In particular, it highlights the vital role performed by medical informatics in supporting doctors in the identification of diseases as well as the formulation of suitable choices via the use of data management and knowledge discovery. As part of the intelligent control method, it is proposed in this research to construct a Histogram Equalization (HE)-based Adaptive Center-Weighted Median (ACWM) filter, which is then used to segment/detect the OM in tympanic membrane images using different segmentation methods in order to minimise noise and improve the image quality. A tympanic membrane dataset, which is freely accessible, was used in all experiments.

Design of Exponentially Weighted Median Filter Cascaded With Adaptive Median Filter

The objective of this paper is to design an II phase algorithm employing median filters for enlightening the performance in removing impulse noise during the processing of the image. The cascaded filter section employs an Adaptive median filter in the first phase followed by a Recursive weighted median filter (RWM) in the second phase. The RWM filter weight is selected with the Median Controlled Algorithm. As a design parameter, the exponential weights of RWM filters are used in the feedback path. The projected algorithm can achieve suggestively improved quality of image when compared to fixed weight or the Center Weighted Median filters.

Resonance Image Enhancement using Hybrid Center Weighted Median Filter and Bacteria Foraging Optimization

Magnetic Resonance Image (MRI) is the best imaging technique employed nowadays for diagnosing brain tumour in the initial stage. This paper recommends an unique method for the brain MR image enhancement, that is centred on the Hybrid Center Weighted Median (HCWM) filter and Bacteria Foraging Optimization (BFO). The MR image for this research is obtained from the online and it is pre-processed to remove all the film artifacts. After that the high frequency components are eliminated from the MR brain image by means of a newly proposed HCWM filter. HCWM Filter is the hybrid filter derived by combining the Center Weighted Median Filter and the Weiner Filter. The swarm-based intelligence algorithm called the bacteria foraging optimization is used to predict the weights of the filter dynamically. The performance of the proposed filtering approach is evaluated with the other available filtering methods.

A Robust De-Noising Model for Image Enhancement with Adaptive Median Filtering

This paper presents a robust de-noising model for image enhancement using adaptive median filtering. In this approach image noise is detected with a standard median filter using an adaptive window. Within the window, the original value of the center pixel is changed to a newer that is closer to or same as the standard median. A comparison has been arranged among the proposed method, the standard median (SM) filter and the center weighted median (CWM) filter, which proves the superiority of the proposed filtering method.

An efficient median filter based method for removing random-valued impulse noise

In this paper, we propose a two-phase median filter based iterative method for removing random-valued impulse noise. In the first phase, we use the adaptive center-weighted median filter to identify pixels which are likely to be corrupted by noise (noise candidates). In the second phase, these noise candidates are restored using a median filter based iterative method which allows edges and noise-free pixels to be preserved. These two phases are applied alternatively. Simulation results indicate that the proposed method performs better than many well-known methods while preserving its simplicity.

Describe a new type of mean filter and its application in pressure distribution

A new center-weighted median and mean filter algorithm with self-adaptive time-space filter window was proposed.The algorithm combined weighted center median filter with weighted mean filter for denoising Gaussian noise and salt-pepper noise.The experimental results show that this algorithm can effectively remove the mixed noise in the pressure distribution data,while its computing time is less than that of other linear algorithms of wavelet,Wiener filter and so on.

Adaptive Window Length Recursive Weighted Median Filter for Removing Impulse Noise in Images with details Preservation

An adaptive window length Recursive Weighted Median filter [ARWMF] for removing the impulse noise with better edge and ¯ne detail preservation is presented. Larger window size may blur the images and the lower window size does not remove the noise at high density. To overcome this, the window size of the RWMF is adaptive based on the presence of noise density. Median controlled algorithm is used to calculate the weights for the RWMF. In median controlled algorithm, the ¯lter gives the smallest weight for the impulse. However, for many weight functions, including the exponential one, this weight is non-zero. Thus the impulse has an effect on the output and the magnitude of the impulse is reduced. The computational complexity for the weight calculation is simple and it is very efficient. The window size of the RWMF is adaptive based on the presence of noise density. The proposed algorithm produces better edge and fine details preservations and reduces blurring at the high density impulse noise.The performance of the proposed algorithm is given in terms of mean square error (MSE), mean absolute error (MAE) and peak signal to noise ratio (PSNR) and it is compared with Standard Median ¯lters, Weighted Median filters, Center Weighted Median ¯lters, Recursive Weighted Median filters and Lins Adaptive length Recursive weighted median filters using Median Controlled Algorithm.

An Iterative Procedure for Removing Random-Valued Impulse Noise

This work proposes a two-stage iterative method for removing random-valued impulse noise. In the first phase, we use the adaptive center-weighted median filter to identify pixels which are likely to be corrupted by noise (noise candidates). In the second phase, these noise candidates are restored using a detail-preserving regularization method which allows edges and noise-free pixels to be preserved. These two phases are applied alternatively. Simulation results indicate that the proposed method is significantly better than those using just nonlinear filters or regularization only.

Multi-stage Detection based Filtering for Removal of Impulse Noise from Images

This paper presents a new technique for removal of impulse noise from images. By incorporating the rank-conditioned median filtering, a threshold operation on the deviation of the extreme ranks from the healthy pixel-values in the filtering window and the center-weighted median (CWM) based adaptive impulse detection mechanism into the detection framework. An improved impulse detection scheme is developed. The proposed detection scheme followed by rank-ordered mean (ROM) filtering of corrupted pixels effectively reduces impulse noise while preserving the image details. Simulations show that the proposed scheme works better than the existing schemes in suppressing both fixed-valued and random-valued impulses at different noise ratios.

Space variant median filters for the restoration of impulse noise corrupted images

This brief proposes a generalized framework of median based switching schemes, called multi-state median (MSM) filter. By using a simple thresholding logic, the output of the MSM filter is adaptively switched among those of a group of center weighted median (CWM) filters that have different center weights. As a result, the MSM filter is equivalent to an adaptive CWM filter with a space varying center weight which is dependent on local signal statistics. The efficacy of the proposed filter has been evaluated by extensive simulations.

Application of partition-based median type filters for suppressing noise in images

An adaptive median based filter is proposed for removing noise from images. Specifically, the observed sample vector at each pixel location is classified into one of M mutually exclusive partitions, each of which has a particular filtering operation. The observation signal space is partitioned based an the differences defined between the current pixel value and the outputs of CWM (center weighted median) filters with variable center weights. The estimate at each location is formed as a linear combination of the outputs of those CWM filters and the current pixel value. To control the dynamic range of filter outputs, a location-invariance constraint is imposed upon each weighting vector. The weights are optimized using the constrained LMS (least mean square) algorithm. Recursive implementation of the new filter is then addressed. The new technique consistently outperforms other median based filters in suppressing both random-valued and fixed-valued impulses, and it also works satisfactorily in reducing Gaussian noise as well as mixed Gaussian and impulse noise.

Adaptive impulse detection using center-weighted median filters

Previous median-based impulse detection strategies tend to work well for fixed-valued impulses but poorly for random-valued impulse noise, or vice versa. This letter devises a novel adaptive operator, which forms estimates based on the differences between the current pixel and the outputs of center-weighted median (CWM) filters with varied center weights. Extensive simulations show that the proposed scheme consistently works well in suppressing both types of impulses with different noise ratios.

Nonlinear vector error tolerant interpolation of intermediate video images by weighted medians

The conversion of one video standard into another with different field and scan rates is a key feature for modern TV receivers and multimedia video equipment. Therefore, a new vector-based nonlinear conversion algorithm has been developed which applies nonlinear center weighted median (CWM) filters and yields a very good interpolation quality. One of the main properties of this algorithm is vector error tolerance. This property will be derived in this paper and its advantages will be shown. Assuming a 2-channel model of the human visual system with different spatio-temporal characteristics, there are contrary demands for the CWM filters. One can meet these demands by a vertical band separation and an application of the so-called temporally and spatially dominated CWMs. Hereby interpolation errors of the separated channels can be compensated by an adequate splitting of the spectrum. By this means, a very robust vector error tolerant upconversion method can be achieved which significantly improves the interpolation quality. By an appropriate choice of the CWM filter root structures main picture elements are interpolated correctly also if faulty vector fields occur. To demonstrate the correctness of the deduced interpolation scheme picture content is classified. These classes are distinguished by correct or incorrect vector assignment and correlated or noncorrelated picture content. The mode of operation of the new algorithm is portrayed for each class. Whereas for correlated picture content the mode of operation can be shown by object models the operation mode is shown for noncorrelated picture content by the output probability distribution function of the applied CWM filters. The new algorithm has been verified for a 100 Hz upconversion by objective evaluation methods and by comprehensive subjective test series. Within these tests for critical test sequences a gain of about 2 dB PSNR in the objective tests and about 0.4 evaluation grades in the subjective test series could be achieved.

Tri-state median filter for image denoising

In this work, a novel nonlinear filter, called tri-state median (TSM) filter, is proposed for preserving image details while effectively suppressing impulse noise. We incorporate the standard median (SM) filter and the center weighted median (CWM) filter into a noise detection framework to determine whether a pixel is corrupted, before applying filtering unconditionally. Extensive simulation results demonstrate that the proposed filter consistently outperforms other median filters by balancing the tradeoff between noise reduction and detail preservation.

Analysis of two-dimensional center weighted median filters

Center weighted median (CWM) filters, which have been recognized as detail preserving filters, are an important and the simplest subclass of weighted median (WM) filters. In this paper, we analyze the root signals of two-dimensional (2-D) CWM filters. In particular, we derive the required form for a signal to be a root of a 2-D CWM filter. The required form of signals to be roots is then used to evaluate the detail preserving properties of 2-D CWM filters. As examples, the detail preserving properties of some 2-D CWM filters are compared with other detail preserving filters, i.e. multilevel median filters. The generation of binary root signals of some 2-D CWM filters is treated in the term of the smallest surviving object (SSO). It is illustrated by some examples that CWM filters with different orientation of windows can be useful in image segmentation.

Center weighted median filters: Some properties and their applications in image processing

Abstract Several deterministic properties of center weighted median (CWM) filters are analyzed in this paper. The root structures of CWM filters are derived. A test is devised to check whether a signal is a root of a given CWM filter. The convergence behaviour of recursive and nonrecursive CWM filters is then analyzed. In particular, based on their root structures, it is proven that repeated filtering on any appended finite length signal by any CWM filter produces roots in a finite number of filter passes. A synthesis method of CWM filters under structural constraints is presented. A separable CWM filter is proposed as an application of one-dimensional CWM filters in image processing. It is expected that by using CWM filters, more details can be preserved along the horizontal and vertical directions. Adaptive separable CWM filters are then proposed. We show that they can be better than two-dimensional adaptive CWM filters with the same window size in some cases. Adaptive CWM filters are then extended to adaptive symmetric weighted median filters.

Image sequence enhancement based on adaptive symmetric order statistics

Statistical properties of the spatiotemporal center-weighted median (CWM) filter for image sequences are investigated. It is statistically shown that the CWM filter preserves image structures under motion at the expense of noise suppression. To improve the CWM filter so it can be effectively used in image sequence processing, the adaptive directional CWM filter (ADCWM) is proposed. This filter utilizes a multistage filtering structure, based on adaptive symmetric order statistic (ASOS) operators which produce a pair of order statistics symmetric about the median. The ASOSs are selected by using adaptive parameters adjusted by local image statistics. It is shown experimentally that the proposed filter can preserve image structures while attenuating noise without the use of motion estimation.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

Center weighted median filters and their applications to image enhancement

The center weighted median (CWM) filter, which is a weighted median filter giving more weight only to the central value of each window, is studied. This filter can preserve image details while suppressing additive white and/or impulsive-type noise. The statistical properties of the CWM filter are analyzed. It is shown that the CWM filter can outperform the median filter. Some relationships between CWM and other median-type filters, such as the Winsorizing smoother and the multistage median filter, are derived. In an attempt to improve the performance of CWM filters, an adaptive CWM (ACWM) filter having a space varying central weight is proposed. It is shown that the ACWM filter is an excellent detail preserving smoother that can suppress signal-dependent noise as well as signal-independent noise. >