Center Weighted Median Filter Research Articles

Development of a New Numerical Conjugate Gradient Technique for Image Processing

We present a new iterative conjugate gradient technique for image processing. The technique is based on a new derivation of the conjugacy coefficient and develops a variant of the classical Fletcher-Reeves conjugate gradient method. The derivation exploits a quadratic function model. The new method is intended to minimize the presence of noise by utilizing the adaptive median filter (AMF) to reduce salt-and-pepper noise, while the adaptive center-weighted median filter (ACWMF) is used to reduce random-valued noise. The theoretical convergence properties of the method are proven and then tested on a basic set of images using MATLAB. The results show that the proposed algorithm is more efficient than the classical Fletcher-Reeves (FR) method, as measured by the signal-to-noise ratio (PSNR). The number of iterations and the number of function evaluations are also lower for the proposed method. The favorable performance of the new algorithm provides promise for deriving similar techniques that enhance the speed and efficiency of image-processing libraries.

An efficient method to remove mixed Gaussian and random-valued impulse noise.

Mixed Gaussian and Random-valued impulse noise (RVIN) removal is still a big challenge in the field of image denoising. Existing denoising algorithms have defects in denoising performance and computational complexity. Based on the improved “detecting then filtering” strategy and the idea of inpainting, this paper proposes an efficient method to remove mixed Gaussian and RVIN. The proposed algorithm contains two phases: noise classification and noise removal. The noise classifier is based on Adaptive center-weighted median filter (ACWMF), three-sigma rule and extreme value processing. Different from the traditional “detecting then filtering” strategy, a preliminary RVIN removal step is added to the noise removal phase, which leads to three steps in this phase: preliminary RVIN removal, Gaussian noise removal and final RVIN removal. Firstly, RVIN is processed to obtain a noisy image approximately corrupted by Gaussian noise only. Subsequently, Gaussian noise is re-estimated and then denoised by Block Matching and 3D filtering method (BM3D). At last, the idea of inpainting is introduced to further remove RVIN. Extensive experimental results demonstrate that the proposed method outperforms quantitatively and visually to the state-of-the-art mixed Gaussian and RVIN removal methods. In addition, it greatly shortens the computation time.

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.

An Accession for Image Denoising by Multiple Filter and Correlative Examination for Random Impulse Noise

Denoising an image is a significant problem in the processing of digital images. Any impulse noise damages the image and the aim of denoising is to remove noise and restore the high-quality image as much as possible. This paper aims to develop a method to discriminate between corrupted and uncorrupted pixels and develop a novel filter to denoise the image. It is also necessary to consider images with different level of noise of various applications to develop an optimal system to remove the noise for further processing. In this paper different filtering techniques such as Median-Filter (MF), Weighted -Median-Filter (WMF),Centre-Weighted Median filter (CWMF), and adaptive centre weighted median filter (ACWMF) are used for denoising, also a novel filter Asymmetric Trimmed Median Filter(ATMF) is designed which outperforms compared to the filters designed earlier. Experimentation is carried by considering the dataset size of 700 noisy images. The average PSNR value of proposed system is 28.12%(DB).

Combined Centre-Weighted Median Filter and BM3D to Filter Digital Images in Mixed Gaussian and Impulsive Environments

This paper studies the combination of a centre-weighted median filter with block matching 3-D filtering for digital images with mixed impulsive and Gaussian noise. The proposed combined filter has an extended application range with respect to both components. The work in the paper derives a simple formula for selecting the weight of the centre-weighted median filter. Adaptive weights are given for neighbourhoods 3 × 3 and wider as functions of the impulsive noise percentage and Gaussian noise strength. Simulation results confirm the effectiveness of the proposed combined filter.

Renovating Contaminative Image Archives Based on Patch Propagation and Adaptive Confidence Collation

The preservation of classical archives is an essential task in the digitization of culture. So far, there remain two crucial challenges in archiving treasurable but aged image records. The first one is to eliminate a large-scale smudge located in a smooth ground, while the second one is to improve the readability of text content. Undoubtedly, the quality of an aged image is always degraded by antiquated smears, and the same situation occurs to obscure the content of a text image. In this paper, the authors apply the concept of patch propagation to help renovating contaminative images and introduce an adaptive confidence collation strategy to avoid the phenomenon of error diffusion, which often results in serious distortion in repairing. Furthermore, the techniques of Canny detection and center-weighted median filter are adopted in the proposed mechanism to refine the blurry target. In particular, the obscured text of aged images can be improved in the new mechanism, while it is seldom concerned in related works.

基于分层滤波技术的冲击噪声检测与去除算法

本文提出了一种基于分层滤波技术的图像随机值冲击噪声检测和去除算法。为了解决经典自适应中心加权中值滤波技术在较高噪声比率下漏检严重的问题，我们采取逐级设置由高到低的噪声值判决门限来层层筛选噪声，利用不同高度的门限甄别出不同信赖度的冲击噪点，同时更新已检测出的噪声点值。每一层均得到一幅中间去噪图像，并继续进行噪声点的判决，最终得到完整的噪声点空间以及终极去噪图像。通过对多幅不同噪声比率的噪声图进行图像恢复的仿真实验验证后，得出结论：本文所提算法无论在低噪声比率还是高噪声比率的情况下均能有效地检测出图像中的随机值冲击噪声，漏检和误检像素数相对较低，同时获得了较好的图像去噪效果，极大地扩展了原算法的应用范围。 In this paper, an algorithm about random-valued impulse noise detection and removal based on the hierarchical filter technology is proposed. In order to solve the problem of severe miss detection of classical adaptive center-weighted median filter in the situation of high noise ratios, we set the noise judgment thresholds from high values to low values to select noisy pixels hierarchically. The impulse noise with different reliable degrees is selected through different thresholds and at the same time the detected pixel values are updated. The interim denoised image is generated in each denoising layer and the noise detection continues in the next denoising layer. Lastly, we get all the locations of noisy pixels and the final denoised image is obtained. Extensive experimental results for different noisy images with different noise ratios show that our proposed algorithm can obtain the good performance of random-valued impulse noise detection in the situations of not only low noise ratios but also high noise ratios. The miss and false noise detection ratios are both low relatively. At the same time, the good denoised images can be obtained. Our algorithm expands the application range of the classical adaptive center-weighted median filter.

PERFORMANCE ANALYSIS OF IMAGE FILTERING ALGORITHMS FOR MRI IMAGES

In Image Processing, the digital images are much sensitive to noise which results due to the Image Acquisition errors and transmission errors.MRI images captured usually are prone to speckle noise, Gaussian noise, salt and pepper noise etc., Image filtering algorithms are applied over the noisy images to remove the noise and preserve the image details. In this work three different filtering algorithms such as Median filter (MF), Weiner filter (WF) and Center Weighted Median filter (CWM) are used to remove the noise present in the MRI images. The performance of these filters are compared using the statistical parameters such as Mean Square Error(MSE) and Peak Signal to Noise Ratio(PSNR).The study shows that the Weiner filters reconstructs a high quality image than Median and Center weighted Median filter.

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.

A median/ANFIS filter for efficient restoration of digital images corrupted by impulse noise

A new operator for removing impulse noise from digital images is presented. The proposed operator is a hybrid filter constructed by combining four center-weighted median filters (CWMF) with a simple adaptive neuro-fuzzy inference system (ANFIS). The internal parameters of the ANFIS are optimized by training. The training is performed by using simple artificial images which can easily be generated by computer. The fundamental advantage of the proposed operator over other operators in the literature is that it efficiently removes impulse noise while at the same time effectively preserves image details and texture. Performance of the proposed operator is tested at various noise densities and for different test images, and also compared with conventional as well as state-of-the-art impulse noise removal operators. Experimental results show that the proposed operator significantly outperforms the other operators and efficiently removes impulse noise from digital images without distorting image details and texture.

Removal of Gaussian Noise from Degraded Images in Wavelet Domain

Observed images are often corrupted by Gaussian noise. If the image is embedded in small-amplitude Gaussian noise, the noise can be removed by applying a Wiener filter. Recently, the BayesShrink wavelet method has attracted considerable attention as a denoising technique. In this paper, we propose a method for removal of Gaussian noise of large amplitude as well as of small amplitude which cannot be removed only by exploiting the BayesShrink wavelet method. Our approach is a combination of the BayesShrink wavelet method with the directional adaptive center-weighted median filter. Applying the proposed method to an image corrupted by large-amplitude Gaussian noise, a clean image can be obtained. © 2008 Wiley Periodicals, Inc. Electron Comm Jpn, 91(1): 11–18, 2008; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/eej.10029

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.

Image detail-preserving filter for impulsive noise attenuation

A new nonlinear filter is proposed for attenuating impulsive noise while preserving image details. The filter truncates the grey value of a pixel to the maximal or minimal value of its enclosed surrounding band. Impulsive noise inside the band is thus attenuated while image details are preserved as long as they stretch to the band. The recursive form of the proposed filter leads to a simple architecture for fast implementation. Theoretical analysis and experimental results demonstrate the effectiveness of this new filter for both noise attenuation and detail preservation. For moderately contaminated images, as shown in the experiments, the proposed filter outperforms the standard median filter, the centre-weighted median filter and the unidirectional multistage median filter in terms of mean absolute error and filtering speed.

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.

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.

Moving median filters for area-detector data

Moving median and center-weighted median filters are employed to remove impulse noise from two-dimensional area-detector data. Two generalized mean filters, which have been used previously in crystallographic data reduction, are compared with median filters. Examination of the smoothed data indicates that the median filters are preferred, in this work, over the generalized mean filters, for data with a high frequency of impulse noise. Like the generalized mean filters, the median filters are useful for eliminating the sharp features (spikes, holes etc.) in the area-detector data while retaining the broader peak-like features.

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.