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Showing papers on "Median filter published in 2001"


Journal ArticleDOI
TL;DR: A novel adaptive operator is devises, which forms estimates based on the differences between the current pixel and the outputs of center-weighted median (CWM) filters with varied center weights, which consistently works well in suppressing both types of impulses with different noise ratios.
Abstract: 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.

741 citations


Journal ArticleDOI
TL;DR: A novel switching-based median filter with incorporation of fuzzy-set concept, called the noise adaptive soft-switching median (NASM) filter, to achieve much improved filtering performance in terms of effectiveness in removing impulse noise while preserving signal details and robustness in combating noise density variations.
Abstract: Existing state-of-the-art switching-based median filters are commonly found to be nonadaptive to noise density variations and prone to misclassifying pixel characteristics at high noise density interference. This reveals the critical need of having a sophisticated switching scheme and an adaptive weighted median filter. We propose a novel switching-based median filter with incorporation of fuzzy-set concept, called the noise adaptive soft-switching median (NASM) filter, to achieve much improved filtering performance in terms of effectiveness in removing impulse noise while preserving signal details and robustness in combating noise density variations. The proposed NASM filter consists of two stages. A soft-switching noise-detection scheme is developed to classify each pixel to be uncorrupted pixel, isolated impulse noise, nonisolated impulse noise or image object's edge pixel. "No filtering" (or identity filter), standard median (SM) filter or our developed fuzzy weighted median (FWM) filter will then be employed according to the respective characteristic type identified. Experimental results show that our NASM filter impressively outperforms other techniques by achieving fairly close performance to that of ideal-switching median filter across a wide range of noise densities, ranging from 10% to 70%.

598 citations


Journal ArticleDOI
TL;DR: The digital TV filter is a data dependent lowpass filter, capable of denoising data without blurring jumps or edges, which solves a global total variational (or L(1)) optimization problem, which differs from most statistical filters.
Abstract: Motivated by the classical TV (total variation) restoration model, we propose a new nonlinear filter-the digital TV filter for denoising and enhancing digital images, or more generally, data living on graphs. The digital TV filter is a data dependent lowpass filter, capable of denoising data without blurring jumps or edges. In iterations, it solves a global total variational (or L/sup 1/) optimization problem, which differs from most statistical filters. Applications are given in the denoising of one dimensional (1-D) signals, two-dimensional (2-D) data with irregular structures, gray scale and color images, and nonflat image features such as chromaticity.

513 citations


Journal ArticleDOI
TL;DR: Improved visualization performance after processing the 3D images is demonstrated with two examples, tomographic reconstructions of chromatin and of a mitochondrion, and a novel technique for noise reduction based on nonlinear anisotropic diffusion is proposed.

426 citations


Journal ArticleDOI
TL;DR: In this article, a generalized framework of median based switching schemes, called multi-state median (MSM) filter, is proposed 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 with different center weights.
Abstract: 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.

380 citations


Journal ArticleDOI
TL;DR: An efficient implementation of correlation based disparity calculation with high speed and reasonable quality that can be used in a wide range of applications or to provide an initial solution for more sophisticated methods is presented.
Abstract: This paper presents an efficient implementation for correlation based stereo. Research in this area can roughly be divided in two classes: improving accuracy regardless of computing time and scene reconstruction in real-time. Algorithms achieving video frame rates must have strong limitations in image size and disparity search range, whereas high quality results often need several minutes per image pair. This paper tries to fill the gap, it provides instructions how to implement correlation based disparity calculation with high speed and reasonable quality that can be used in a wide range of applications or to provide an initial solution for more sophisticated methods. Left-right consistency checking and uniqueness validation are used to eliminate false matches. Optionally, a fast median filter can be applied to the results to further remove outliers. Source code will be made publicly available as contribution to the Open Source Computer Vision Library, further acceleration with SIMD instructions is planned for the near future.

318 citations


Journal ArticleDOI
TL;DR: A novel approach for color image denoising is proposed, based on separating the color data into chromaticity and brightness, and then processing each one of these components with partial differential equations or diffusion flows.
Abstract: A novel approach for color image denoising is proposed in this paper. The algorithm is based on separating the color data into chromaticity and brightness, and then processing each one of these components with partial differential equations or diffusion flows. In the proposed algorithm, each color pixel is considered as an n-dimensional vector. The vectors' direction, a unit vector, gives the chromaticity, while the magnitude represents the pixel brightness. The chromaticity is processed with a system of coupled diffusion equations adapted from the theory of harmonic maps in liquid crystals. This theory deals with the regularization of vectorial data, while satisfying the intrinsic unit norm constraint of directional data such as chromaticity. Both isotropic and anisotropic diffusion flows are presented for this n-dimensional chromaticity diffusion flow. The brightness is processed by a scalar median filter or any of the popular and well established anisotropic diffusion flows for scalar image enhancement. We present the underlying theory, a number of examples, and briefly compare with the current literature.

210 citations


Journal ArticleDOI
TL;DR: A generic n-dimensional filter with the primary purpose of eliminating impulsive-like noise is presented and is found to be much faster than the median filter while performing comparably in terms of both image information conservation and noise reduction, which suggests that it could replace the Median filter for the preliminary processing included in state-of-the-art noise removal filters.
Abstract: A generic n-dimensional filter with the primary purpose of eliminating impulsive-like noise is presented. This recursive nonlinear filter is composed of two conditional rules, which are applied independently, in any order, one after the other. It identifies noisy items by inspection of their surrounding neighborhood, and afterwards it replaces their values with the most "conservative" ones out of their neighbors' values. In this way, no new values are introduced and the histogram distribution range is conserved. This n-dimensional filter can be decomposed recursively to a lower dimensional space, each time generating two sets of n(n-1)-dimensional filters. This study, which focuses on the case of two-dimensional signals (gray scale images), explores one possible implementation of this new filter and orients the evaluation of its performance toward the median filter, as this filter is the basis of many more sophisticated filters for impulsive noise reduction. Tests were carried out using both real and artificial images. We found this new filter to be much faster than the median filter while performing comparably in terms of both image information conservation and noise reduction, which suggests that it could replace the median filter for the preliminary processing included in state-of-the-art noise removal filters. This new filter should either eliminate or attenuate most noisy pixels in synthetic and natural images not excessively contaminated. It has a slight smoothing effect on nonnoisy image regions. In addition, it is scalable, easily implemented, and adaptable to specific applications.

208 citations


Journal ArticleDOI
TL;DR: The proposed denoising methods are optimal over the Huber /spl epsi/-contaminated normal neighborhood and are highly resistant to outliers.
Abstract: Nonlinear filtering techniques based on the theory of robust estimation are introduced. Some deterministic and asymptotic properties are derived. The proposed denoising methods are optimal over the Huber /spl epsi/-contaminated normal neighborhood and are highly resistant to outliers. Experimental results showing a much improved performance of the proposed filters in the presence of Gaussian and heavy-tailed noise are analyzed and illustrated.

181 citations


Journal ArticleDOI
TL;DR: In this paper, open-loop noise reduction techniques can be effective in separating partial discharge (PD) signals from noise, through the use of thresholding of wavelet coefficients, which provides near optimal noise reduction for many classes of signals including PD signals in noise.
Abstract: The paper shows that open-loop noise reduction techniques can be effective in separating partial discharge (PD) signals from noise. Wavelet-based denoising through the use of thresholding of wavelet coefficients provides near optimal noise reduction for many classes of signals including PD signals in noise. The effectiveness of wavelet transform-based noise reduction depends on selection of an appropriate wavelet basis function as well as on careful selection of threshold function and levels.

126 citations


Journal ArticleDOI
TL;DR: Several important properties of the myriad filter are introduced and its optimality in the family of /spl alpha/-stable distributions is proved.
Abstract: Linear filtering theory has been largely motivated by the characteristics of Gaussian signals. In the same manner, the proposed myriad filtering methods are motivated by the need for a flexible filter class with high statistical efficiency in non-Gaussian impulsive environments that can appear in practice. We introduce several important properties of the myriad filter and prove its optimality in the family of /spl alpha/-stable distributions.

Book ChapterDOI
TL;DR: A novel time-selection strategy for iterative image restoration techniques: the stopping time is chosen so that the correlation of signal and noise in the filtered image is minimised.
Abstract: We develop a novel time-selection strategy for iterative image restoration techniques: the stopping time is chosen so that the correlation of signal and noise in the filtered image is minimised. The new method is applicable to any images where the noise to be removed is uncorrelated with the signal; no other knowledge (e.g. the noise variance, training data etc.) is needed. We test the performance of our time estimation procedure experimentally, and demonstrate that it yields near-optimal results for a wide range of noise levels and for various filtering methods.

Proceedings ArticleDOI
08 Dec 2001
TL;DR: This work focuses on the local mode, the more commonly studied global mode, which preserves edges and details and is easily extensible to multi-channel data and results on color images include successful noise attenuation while preserving edges and detail by local mode filtering.
Abstract: Linear filters have two major drawbacks. First, edges in the image are smoothed with increasing filter size. Second, by extending the filters to multi-channel data, correlation between the channels is lost. Only a few researchers have explored the possibilities of mode filtering to overcome these problems. Mode filtering is motivated from both a local histogram with tonal scale and a robust statistics point of view. The tonal scale is proved to be equal to the scale of the error norm function within the robust statistics framework. Instead of the more commonly studied global mode, our focus is on the local mode. It preserves edges and details and is easily extensible to multi-channel data. A generalization of the spatial Gaussian filtering to a spatial and tonal Gaussian filter is used to iterate to the local mode. Results on color images include successful noise attenuation while preserving edges and detail by local mode filtering.

Patent
03 May 2001
TL;DR: In this paper, a video encoder regulates the level of a buffer (e.g., how full or empty the buffer is) by adjusting median filtering of video information (i.e., pixel data and/or prediction residuals).
Abstract: An encoder dynamically filters information for lossy compression so as to control bitrate or quality with few sudden, dramatic changes to perceptual quality of the compressed information. For example, a video encoder regulates the level of a buffer (e.g., how full or empty the buffer is) by adjusting median filtering of video information (e.g., pixel data and/or prediction residuals). The buffer stores compressed video information for the video encoder. Based upon the buffer level, the video encoder changes the median filter kernel applied to video information. If the buffer starts to get too full, the video encoder increases the size of the kernel, which tends to smooth the video information, introduce slight blurriness, and deplete the buffer. If the buffer starts to get too empty, the video encoder decreases the size of the kernel or stops filtering, which tends to preserve the video information and fill the buffer.

Patent
17 May 2001
TL;DR: In this paper, a fuzzy logic process, pixel deltas, and dual ramp generators are used to determine the horizontal and vertical length of a processing window surrounding an image block boundary.
Abstract: A filter reduces artifacts, such as grid noise and staircase noise, in block-coded digital images with image block boundaries. The type of filtering is determined after an estimation of the image global metrics and local metrics. For areas of the image near grid noise, the filter performs low pass filtering. For image fine details, such as edges and texture, no filtering is performed so that masking is avoided. The filter operates in intra-field mode and uses a fuzzy logic process, pixel deltas, and dual ramp generators to determine the horizontal and vertical length of a processing window surrounding an image block boundary.

Proceedings ArticleDOI
04 Nov 2001
TL;DR: In this article, a segmented logarithmic transform was proposed for the stabilization of the non-stationary noise in the projection data and a two-dimensional Wiener filter was designed for an analytical treatment of the noise.
Abstract: Projection data acquired for image reconstruction of low-dose computed tomography (CT) are degraded by many factors. These factors complicate noise analysis on the projection data and render a very challenging task for noise reduction. In this study, we first investigate the noise property of the projection data by analyzing a repeatedly acquired experimental phantom data set, in which the phantom was scanned 900 times at a fixed projection angle. The statistical analysis shows that the noise can be regarded as normally distributed with a nonlinear signal-dependent variance. Based on this observation, we then utilize scale transformations to modulate the projection data so that the data variance can be stabilized to be signal independent. By analyzing the relationship between the data standard deviation and the data mean level, we propose a segmented logarithmic transform for the stabilization of the non-stationary noise. After the scale transformations, the noise variance becomes approximately a constant. A two-dimensional Wiener filter is then designed for an analytical treatment of the noise. Experimental results show that the proposed method has a better noise reduction performance without circular artifacts, by visual judgment, as compared to conventional filters, such as the Harming filter.

Patent
14 Aug 2001
TL;DR: In this paper, a motion compensated temporal filtering using previously generated motion vectors and adaptive spatial filtering at scene change frames is proposed to reduce the noise in a video system by applying motion compensation and adaptive spatio-temporal filtering.
Abstract: Noise is reduced in a video system by applying motion compensated temporal filtering using previously generated motion vectors and adaptive spatial filtering at scene change frames. Various types of noise can be introduced into video prior to compression and transmission. Artifacts arise from recording and signal manipulation, terrestrial or orbital communications, or during decoding. Noise introduced prior to image compression interferes with performance and subsequently impairs system performance. While filtering generally reduces noise in a video image, it can also reduce edge definition leading to loss of focus. Filtering can also tax system throughput, since increased computational complexity often results from filtering schemes. Furthermore, the movement of objects within frames, as defined by groups of pixels, complicates the noise reduction process by adding additional complexity. In addition to improvements made to FIR spatial filtering, the present invention improves on previous filtering techniques by using Infinite Impulse Response (IIR) temporal filtering to reduce noise while maintaining edge definition. It also uses motion vectors previously calculated as part of the first-pass image encoding or alternatively by transcoding to reduce computational complexity for P-frame and B-frame image preprocessing. Single stage P-frame temporal noise filtering and double stage B-frame temporal noise filtering are presented.

Patent
25 Apr 2001
TL;DR: In this article, a method and apparatus for processing image data is described, comprising acquiring a frame of image data and compressing the dynamic range of the frame of images using a dynamic range compression (DRC) algorithm that utilizes down-sampling, median filtering, and up sampling.
Abstract: A method and apparatus for processing image data is described, comprising acquiring a frame of image data and compressing the dynamic range of the frame of image data using a dynamic range compression (DRC) algorithm that utilizes down-sampling, median filtering, and up-sampling. The DRC algorithm comprises down-sampling a frame of image data comprising a first array of pixels to generate a second array of pixels, applying a first median filter to the second array of pixels to generate a blurred array of pixels, up-sampling the blurred array of pixels, and removing at least a portion of low-frequency gradient data generated by previous steps from the frame of image data.

Journal ArticleDOI
TL;DR: An adaptive median based filter is proposed for removing noise from images that consistently outperforms other median based filters in suppressing both random-valued and fixed-valued impulses, and it works satisfactorily in reducing Gaussian noise as well as mixed Gaussian and impulse noise.
Abstract: 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.

Journal ArticleDOI
TL;DR: A new vector median filter suitable for colour image processing is presented, based on a new ordering of vectors in the HSV colour space, which shows promising results in terms of colour image restoration.

Journal ArticleDOI
TL;DR: An algorithm for synthetic aperture radar (SAR) speckle reduction and edge sharpening is introduced and the evaluation of popular filters from the viewpoint of texture preservation.
Abstract: This paper makes two contributions. It first introduces an algorithm for synthetic aperture radar (SAR) speckle reduction and edge sharpening. Existing speckle filtering algorithms can effectively reduce the speckle effect but unfortunately also, to some degree, smear edges and blur images. Even for unfiltered images, there is still a need for edge sharpening; since SAR sensors have limited bandwidths, leading to slow responses to sudden changes (smearing sharp edges). The proposed algorithm functions as an adaptive-mean filter. Edge crossing points are detected by using the second-order derivative of the Gaussian function as a wavelet transform function. A proper dilation scale factor enables the wavelet transform function to detect only edge crossings and ignore the local oscillations. Then in a moving window the mean filter is applied if there is no edge crossing point. Otherwise, averaging is only applied to the part of the window separated by edge crossing points. Consequently, the algorithm smooths uniform areas while it sharpens and enhances edges. Edges of the filtered images are generally sharper than the original. Similarities between the proposed filter and other popular speckle filters, such as the Lee, Kuan, and Frost filters, designated for SAR multiplicative noise, are analyzed. Another contribution of the paper is the evaluation of popular filters from the viewpoint of texture preservation. The evaluation is carried out using the first and second-order histograms. Possible distortions caused by filters are explained.

Journal ArticleDOI
TL;DR: The wavelet transform and noise modeling is discussed, and how to measure the information and the implications for object detection, filtering, and deconvolution are described, in both a statistical and a deterministic way.
Abstract: We present methods used to measure the information in an astronomical image, in both a statistical and a deterministic way. We discuss the wavelet transform and noise modeling, and describe how to measure the information and the implications for object detection, filtering, and deconvolution. The perspectives opened up by the range of noise models, catering for a wide range of eventualities in physical science imagery and signals, and the new two-pronged but tightly coupled understanding of the concept of information have given rise to better quality results in applications such as noise filtering, deconvolution, compression, and object (feature) detection. We have illustrated some of these new results in this article. The theoretical foundations of our perspectives have been sketched out. The practical implications, too, are evident from the range of important signal processing problems which we can better address with this armoury of methods. The results described in this work are targeted at information and at relevance. While we have focused on experimental results in astronomical image and signal processing, the possibilities are apparent in many other application domains.

Journal ArticleDOI
TL;DR: The robust Wigner distribution is introduced, a reliable TF representation tool for wide class of nonstationary signals corrupted with impulse noise, and produces good accuracy of the instantaneous frequency (IF) estimation.
Abstract: The Wigner distribution (WD) produces highly concentrated time-frequency (TF) representation of nonstationary signals. It may be used as an efficient signal analysis tool, including the cases of frequency modulated signals corrupted with the Gaussian noise. In some applications, a significant amount of impulse noise is present. Then, the WD fails to produce satisfactory results. The robust periodogram has been introduced for spectral estimation of this kind of noisy signals. It can produce good concentration for pure harmonic signals. However, it is not so efficient in the cases of signals with rapidly varying frequency. This is the motivation for introducing the robust WD. It is a reliable TF representation tool for wide class of nonstationary signals corrupted with impulse noise. This distribution produces good accuracy of the instantaneous frequency (IF) estimation. Using the Huber (1981) loss function, a generalization of the WD is presented. It includes both the standard and the robust WD as special cases. This distribution can be used for TF analysis of signals corrupted with a mixture of impulse and Gaussian noise. The presented theory is illustrated on examples, including applications on the IF estimation and time-varying filtering of signals corrupted with a mixture of the Gaussian and impulse noise. The case study analysis of the IF estimators' accuracy, based on the standard and the robust WD forms, is performed. In order to improve the IF estimation, a median filter is applied on the obtained IF estimate.

Patent
22 Jun 2001
TL;DR: In this article, the authors proposed an adaptive local mean estimator, an economic standard deviation estimator and a minimum-mean-square-error (MMSE) based de-noising technique.
Abstract: The basic configuration of Single local Adaptive Window Spatial Noise Reducer (SAW-SNR) is based on a preliminary de-noising low-pass filter followed by homogenous region segmentation to the considered pixel in a given local window. The configuration is composed also of an adaptive local mean estimator, an adaptive local statistic estimator which is preferably an economic standard deviation (SD) estimator and finally, a minimum-mean-square-error (MMSE) based de-noising technique. The proposed segmentation configuration outperforms existing spatial noise reducers in term of subjective and objective performances, in term of edge preservation, noise reduction in both homogenous regions or picture edges and Peak Signal to noise Ratio (PSNR). A second configuration in the form of a Parallel Multiple local Adaptive Window Spatial Noise Reducer (Parallel M-AW-SNR), is a combination of several basic configurations which implements different segmented windows. The M-AW-SNR, which is the less complex configuration for multiple spatial noise reducers, reduces further residual noise as compared to the basic configuration. A third configuration combines the basic configuration of SAW-SNR with a controllable noise variance estimator. This generic configuration allows an adaptive local control of noise reduction level, which can be useful for some correlated noise such as ringing noise in DCT-based decompressed images or cross-luminance noise in composite decoded images.

Patent
Yoni Perets1
22 Jun 2001
TL;DR: In this paper, a noise flattening filter has a filter response that dynamically adjusts based on the current noise spectrum in a wireless channel, which is estimated and used to determine a noise classification for the channel.
Abstract: A communication device includes a noise flattening filter having a filter response that dynamically adjusts based on the current noise spectrum in a wireless channel. The noise spectrum of the wireless channel is estimated and used to determine a noise classification for the channel. A noise flattening filter response is then selected based upon the noise classification for use in filtering signals received from the channel. The filtered signals are then delivered to an equalizer for further processing.

Patent
10 Dec 2001
TL;DR: In this article, the authors proposed a method of removing noise from a digital image including receiving an original digital image, including a plurality of pixels, generating at least one residual digital image from the original image, the base digital image having a lower spatial resolution than the original one.
Abstract: A method of removing noise from a digital image including receiving an original digital image including a plurality of pixels; generating at least one residual digital image and at least one base digital image from the original digital image, the base digital image having a lower spatial resolution than the original digital image; and generating a noise reduced base digital image by removing noise from the base digital image with a noise reduction filter so that when the noise reduced base digital image is combined with the residual digital image to produce a reconstructed digital image, noise is not present in the reconstructed digital image.

Journal ArticleDOI
TL;DR: A fully automated non-rigid image registration method that maximizes a local voxel-based similarity metric and gives comparable registration to mutual information in intra- and inter-modality tasks at full sampling and is superior to Mutual information in registering sparsely sampled images.
Abstract: Conventional approaches to image registration are generally limited to image-wide rigid transformations. However, the body and its internal organs are non-rigid structures that change shape due to changes in the body's posture during image acquisition, and due to normal, pathological and treatment-related variations. Inter-subject matching also constitutes a non-rigid registration problem. In this paper, we present a fully automated non-rigid image registration method that maximizes a local voxel-based similarity metric. Overlapping image blocks are defined on a 3D grid. The transformation vector field representing image deformation is found by translating each block so as to maximize the local similarity measure. The resulting sparsely sampled vector field is median filtered and interpolated by a Gaussian function to ensure a locally smooth transformation. A hierarchical strategy is adopted to progressively establish local registration associated with image structures at diminishing scale. Simulation studies were carried out to evaluate the proposed algorithm and to determine the robustness of various voxel-based cost functions. Mutual information, normalized mutual information, correlation ratio (CR) and a new symmetric version of CR were evaluated and compared. A T1-weighted magnetic resonance (MR) image was used to test intra-modality registration. Proton density and T2-weighted MR images of the same subject were used to evaluate inter-modality registration. The proposed algorithm was tested on the 2D MR images distorted by known deformations and 3D images simulating inter-subject distortions. We studied the robustness of cost functions with respect to image sampling. Results indicate that the symmetric CR gives comparable registration to mutual information in intra- and inter-modality tasks at full sampling and is superior to mutual information in registering sparsely sampled images.

Journal Article
TL;DR: In this paper, a new median based filtering algorithm-extremum median filtering is presented in order not to perturb the efficient signals as much as possible when the noises are removed, the following approaches are developed in this paper First, all the pixels are separated into signal pixels and noise pixels according to the decision criterion given in the following; then, noise pixels are replaced with the median value of their neighborhood in the input image.
Abstract: A new median based filtering algorithm-extremum median filtering is presented In order not to perturb the efficient signals as much as possible when the noises are removed, the following approaches are developed in this paper First, all the pixels are separated into signal pixels and noise pixels according to the decision criterion given in the following; then, noise pixels are replaced with the median value of their neighborhood in the input image The decision criterion: if a pixel value is the extremum (max or min) of its neighborhood, it is a noise pixel; else, it is a signal pixel This decision criterion is under such an assumption: inherent relationships exist among neighbor pixels If a pixel value is far higher or lower than the others' value of its neighborhood are, that is to say, a pixel has lower correlation with its neighbors, we may consider that it had been contaminated with noise Else, if it is similar to the others, we consider that it represents an effective signal Experimental results show that the assumption fits the facts quit wellIn this paper, attention is forcused on filtering of images degraded by "salt and pepper" noises Examples on images containing 184×148 pixels are givenExperimental results show that the EM filtering has better performance than standard median filtering with less subtle details being eliminated The SNR of the image filtered with EM filter is about 4dB higher than that with median filter This is because the operation only affects noise pixels and most of the uncontaminated pixels keep intact Especially,in the case of lower SNR,larger filtering window improves the SNR notably Median filter is not the case, for the filtering operation blurs the image extremely with the increasing of the filtering window

Journal ArticleDOI
TL;DR: A recursive least squares adaptive noise canceling method is applied to estimate electrical signals coming from an accelerometer embedded in a bus under a performance test and achieves an improvement of 20 dB of the signal-to-noise ratio.
Abstract: In this paper a recursive least squares adaptive noise canceling method is applied to estimate electrical signals coming from an accelerometer embedded in a bus under a performance test. Noise and/or interference corrupt this electrical signal, and it is necessary to diminish the noise in measuring the acceleration of the bus. The recursive least squares algorithm used in this paper has the advantage of computational simplicity and is well suited to represent time varying features in real-time measurements of the acceleration. In this experiment the signal of interest and the noise have very close bands of frequency to each other, and it is very difficult to diminish the noise using fixed filters. The experimental results have shown that the adaptive filter acts as an adaptive notch filter whose null points are determined by the frequency of the noise signal, and it achieved an improvement of 20 dB of the signal-to-noise ratio. The experimental results demonstrate the importance of using both analog signal conditioning and digital signal processing when we have to deal with signals corrupted by noise.

Journal ArticleDOI
TL;DR: The robust short-time Fourier transform (STFT) and the robust Wigner distribution (WD), based on the simple median filter, are proposed and their efficiency in time-frequency analysis is demonstrated.