Showing papers on "Median filter published in 1995"

Adaptive median filters: new algorithms and results

Abstract: Based on two types of image models corrupted by impulse noise, we propose two new algorithms for adaptive median filters. They have variable window size for removal of impulses while preserving sharpness. The first one, called the ranked-order based adaptive median filter (RAMF), is based on a test for the presence of impulses in the center pixel itself followed by a test for the presence of residual impulses in the median filter output. The second one, called the impulse size based adaptive median filter (SAMF), is based on the detection of the size of the impulse noise. It is shown that the RAMF is superior to the nonlinear mean L/sub p/ filter in removing positive and negative impulses while simultaneously preserving sharpness; the SAMF is superior to Lin's (1988) adaptive scheme because it is simpler with better performance in removing the high density impulsive noise as well as nonimpulsive noise and in preserving the fine details. Simulations on standard images confirm that these algorithms are superior to standard median filters. >

Interpolation of missing data in image sequences

Abstract: This paper presents a number of model based interpolation schemes tailored to the problem of interpolating missing regions in image sequences. These missing regions may be of arbitrary size and of random, but known, location. This problem occurs regularly with archived film material. The film is abraded or obscured in patches, giving rise to bright and dark flashes, known as "dirt and sparkle" in the motion picture industry. Both 3-D autoregressive models and 3-D Markov random fields are considered in the formulation of the different reconstruction processes. The models act along motion directions estimated using a multiresolution block matching scheme. It is possible to address this sort of impulsive noise suppression problem with median filters, and comparisons with earlier work using multilevel median filters are performed. These comparisons demonstrate the higher reconstruction fidelity of the new interpolators. >

Optimal weighted median filtering under structural constraints

Abstract: A new expression for the output moments of weighted median filtered data is derived. The noise attenuation capability of a weighted median filter can now be assessed using the L-vector and M-vector parameters in the new expression. The second major contribution of the paper is the development of a new optimality theory for weighted median filters. This theory is based on the new expression for the output moments, and combines the noise attenuation and some structural constraints on the filter's behavior. In certain special cases, the optimal weighted median filter can be obtained by merely solving a set of linear inequalities. This leads in some cases to closed form solutions for optimal weighted median filters. Some applications of the theory developed in this paper, in 1-D signal processing and image processing are discussed. Throughout the analysis, some striking similarities are pointed out between linear FIR filters and weighted median filters. >

A new algorithm for image noise reduction using mathematical morphology

Abstract: Morphological openings and closings are useful for the smoothing of gray-scale images. However, their use for image noise reduction is limited by their tendency to remove important, thin features from an image along with the noise. The paper presents a description and analysis of a new morphological image cleaning algorithm (MIC) that preserves thin features while removing noise. MIC is useful for gray-scale images corrupted by dense, low-amplitude, random, or patterned noise. Such noise is typical of scanned or still-video images. MIC differs from previous morphological noise filters in that it manipulates residual images-the differences between the original image and morphologically smoothed versions. It calculates residuals on a number of different scales via a morphological size distribution. It discards regions in the various residuals that it judges to contain noise. MIC creates a cleaned image by recombining the processed residual images with a smoothed version. The paper describes the MIC algorithm in detail, discusses the effects of parametric variations, presents the results of a noise analysis and shows a number of examples of its use, including the removal of scanner noise. It also demonstrates that MIC significantly improves the JPEG compression of a gray-scale image. >

Noise reduction in zero crossing detection by predictive digital filtering

Abstract: A simple combination of nonlinear and linear digital signal processing methods is proposed for efficient noise reduction in zero crossing detectors. The method is very robust against strong impulsive noise, typically encountered in thyristor power converters, where reliable zero crossing detection is required for firing synchronization. A systematic design procedure is described for the proposed filter-based synchronization method, taking into account the specified line frequency tolerance. The fully digital signal processing approach allows compact implementations, and supports flexible interfacing to digital motor control systems. >

Nonlinear multivariate image filtering techniques

Abstract: In this paper, nonlinear multivariate image filtering techniques are proposed to handle color images corrupted by noise. First, we briefly review the principle of reduced ordering (R-ordering) and then define three R-orderings by selecting different central locations. Considering noise attenuation, edge preservation, and detail retention, R-ordering based multivariate filters are designed by combining the R-ordering schemes. To implement color image filtering more effectively, we develop them into a locally adaptive version. The output of the adaptive filter is the closest sample to a central location that is a weighted linear combination of the mean, the marginal median, and the center sample. As a result, we study an adaptive hybrid multivariate (AHM) filter consisting of the mean filter, the marginal median filter, and the identity filter. The performance of the two adaptive filtering techniques is compared with that of some nonadaptive ones. The examples of color image filtering show that the adaptive multivariate image filtering gives a rather good performance improvement. >

Digital filtering for robust 50/60 Hz zero-crossing detectors

Abstract: An improved digital filtering method for line frequency zero-crossing detectors is proposed. The multistage filter efficiently attenuates harmonics, wide-band noise, commutation notches, and other impulsive disturbances without causing any phase shift on the primary sinusoidal waveform. Our novel signal-processing system is a cascade of a median filter and an adaptive sinusoid predictor, followed by up-sampling and interpolation. The three-point median filter effectively removes impulses, and the predictor provides wide-band noise attenuation while compensating for delays in the other processing steps. The predictor adapts to possible line frequency variations within the specified range by changing the set of coefficients, based on an estimate of the instantaneous line frequency. The adaptive approach allows the use of highly selective IIR bandpass predictors.

Adaptive fuzzy multilevel median filter

Abstract: A modified multilevel median filter (MLMF) incorporating the fuzzy associative memory (FAM) system is proposed in this correspondence. The modified method-the adaptive fuzzy multilevel median filter (AFMMF)-overcomes some disadvantages common to most of the conventional MLMF. Experimental results demonstrated that the performance of the AFMMF is superior to that of conventional MLMF in dealing with "short-line-like" noise while preserving edges. >

Adaptive weiner filtering using a dynamic suppression factor

Abstract: An acoustic noise suppression filter including attenuation filtering with a noise suppression factor depending upon the ratio of estimated noise energy of a frame divided by estimated signal energy.

Ultrasound speckle reduction by directional median filtering

Abstract: This paper presents a novel adaptation of the median filter to the problem of boundary-preserving speckle reduction in ultrasonic imaging. The technique involves applying a bank of oriented one-dimensional median filters to the image, and retaining at each point the largest value among all the filter bank outputs. The result is an operator which suppresses speckle noise while retaining the structure of the image, particularly the thin bright streaks, which tend to occur along boundaries between tissue layers. The technique is compared to a block median filter and an algorithm discussed by Loupas et al. (1989), and is shown to be far superior to the median filter, and noticeably better than the Loupas filter at enhancing thin lines.

X-ray fluoroscopy spatio-temporal filtering with object detection

Abstract: One potential way to reduce patient and staff X-ray fluoroscopy dose is to reduce the quantum exposure to the detector and compensate the additional noise with digital filtering. A new filtering method, spatio-temporal filtering with object detection, is described that reduces noise while minimizing motion and spatial blur. As compared to some conventional motion-detection filtering schemes, this object-detection method incorporates additional a priori knowledge of image content; i.e. much of the motion occurs in isolated long thin objects (catheters, guide wires, etc.). We create object-likelihood images and use these to control spatial and recursive temporal filtering such as to reduce blurring the objects of interest. We use automatically computed receiver operating characteristic (ROC) curves to optimize the object-likelihood enhancement method and determine that oriented matched filter kernels with 4 orientations are appropriate. The matched filter kernels are simple projected cylinders. We demonstrate the method on several representative X-ray fluoroscopy sequences to which noise is added to simulate very low dose acquisitions. With processing, we find that noise variance is significantly reduced with slightly less noise reduction near moving objects. We estimate an effective exposure reduction greater than 80%.

Video signal noise reduction system using time-varying filter coefficients.

Abstract: A video signal noise reduction system is disclosed in which corresponding image pixels from a current frame and image pixels from a noise reduced previous frame are averaged to produce respective noise reduced pixel values. In the disclosed exemplary embodiment of the invention, a processed frame memory stores a noise reduced image and a motion estimator identifies a target block of pixels in this processed frame memory to be used in calculating a noise reduced video signal. A motion compensated noise reduction processor combines the block of the noise reduced image pixels stored in the processed frame memory with a current block of pixels generated from the video signal to produce a motion block of noise reduced pixels. The relative weighting applied to the stored pixel and the current pixel depends on the number of pixels that were used to generate the stored pixel. A breakdown detector discards pixels in the motion block if a measure of noise reduction achieved for the one component divided by a measure of noise in the motion block is greater than a threshold value. Sparkle reduction is used to replace any original component of the motion block with its corresponding noise reduced component if all of the components adjacent to the original component have been replaced by their corresponding noise reduced components.

Nonlinear scale-space filtering and multiresolution system

Abstract: We derive and demonstrate a nonlinear scale-space filter and its application in generating a nonlinear multiresolution system. For each datum in a signal, a neighborhood of weighted data is used for clustering. The cluster center becomes the filter output. The filter is governed by a single scale parameter that dictates the spatial extent of nearby data used for clustering. This, together with the local characteristic of the signal, determines the scale parameter in the output space, which dictates the influences of these data on the output. This filter is thus adaptive and data driven. It provides a mechanism for (a) removing impulsive noise, (b) improved smoothing of nonimpulsive noise, and (c) preserving edges. Comparisons with Gaussian scale-space filtering and median filters are made using real images. Using the architecture of the Laplacian pyramid and this nonlinear filter for interpolation, we construct a nonlinear multiresolution system that has two features: (1) edges are well preserved at low resolutions, and (2) difference signals are small and spatially localized. This filter implicitly presents a new mechanism for detecting discontinuities differing from techniques based on local gradients and line processes. This work shows that scale-space filtering, nonlinear filtering, and scale-space clustering are closely related and provides a framework within which further image processing, image coding, and computer vision problems can be investigated. >

Telecommunications speech signal improvement by reduction of residual noise

Abstract: A telecommunications network service overcomes the annoying effects of transmitted noise by signal processing which filters out the noise using a model-based iterative signal estimator. The estimator is provided with a current estimate of the noise power spectral density, using signal frame samples determined by a voice activity detector to be noise-only frames. The signal estimator makes intra-frame iterations of the current frame while using smoothing across LSP parameters of adjacent frames, recent past frames, and up to two contiguous future frames. Non-stationary noise created by the iterative filtering is further reduced in one or more post-filtering stages that use knowledge of the nature of the low level non-stationary noise events.

Filter system and method for efficiently suppressing noise and improving edge definition in a digitized image

Abstract: A filter system and associated method efficiently suppress noise and improve edge definition in a digitized image. The filter system has a statistical noise determination mechanism which initially determines a baseline noise level of the image. The baseline noise level is used by a filter(s) which operates upon the image data at a later time. After the statistical noise determination mechanism, the filter system generally comprises three independent system branches. Each branch has one or more filters. Each filter is configured to receive an image data array and to modify the image data array to derive a respective modified image data array. Further, each branch has a gain control mechanism adapted to modify a respective modified image data array based upon a gain factor. Finally, an assimilation mechanism combines the modified image data arrays from the gain control mechanisms to derive an improved image data array. Optionally, a rescale mechanism may be employed for adjusting intensities corresponding with pixel values of the improved image data array so that the intensities reside within a predefined range, without degradation in image resolution.

TOBI image processing-the state of the art

Abstract: TOBI (Towed Ocean Bottom Instrument) is a deep-tow sidescan sonar vehicle from which sidescan sonar data are now routinely collected and archived. This paper describes the algorithms developed for detailed processing of TOBI data. Sonar imagery has a characteristic set of processing challenges and these are addressed. TOBI provides a very large sonar dataset, and to limit the difficulties of handling and processing these data, the raw data are subjected to a data reduction technique prior to further processing. Slant-range correction is improved by editing vehicle altitude data using a median filter. Noise on TOBI imagery can appear in two main forms; speckle noise and line dropouts. Speckle noise is removed by a small median difference kernel and line dropouts are removed using a ratio of two box-car filters, each with appropriate thresholding techniques. Precise geocoding of the imagery requires an accurate estimate of vehicle location, and a method of calculation is presented. Two optional processing algorithms are also; presented; deblurring of imagery to improve along-track resolution at far range, and the suppression of a surface reflection return which may occur when TOBI is operated in relatively shallow water. Several of the techniques presented can be transcribed and modified to suit other datasets. >

Nearest-neighbour multichannel filter

Abstract: The authors address the problem of noise attenuation for multichannel data. The proposed filter utilises adaptively determined data dependent coefficients. The special case of colour image processing is studied as an important example of multichannel signal processing.

Noise reduction of image sequences using motion compensation and signal decomposition

Abstract: In this paper, a new spatio-temporal filtering method for removing noise from image sequences is proposed. This method combines the use of motion compensation and signal decomposition to account for the effects of object motion. Because of object motion, image sequences are temporally nonstationary, which requires the use of adaptive filters. By motion compensating the sequence prior to filtering, nonstationarities, i.e., parts of the signal that are momentarily not stationary, can be reduced significantly. However, since not all nonstationarities can be accounted for by motion, a motion-compensated signal still contains nonstationarities. An adaptive algorithm based on order statistics is described that decomposes the motion-compensated signal into a noise-free nonstationary part and a noisy stationary part. An RLS filter is then used to filter the noise from the stationary signal. Our new method is experimentally compared with various noise filtering approaches from literature. >

Application of median filtering to noisy data

Abstract: The properties and application of the median filter are investigated using both simulated and real data for signals evolving with time. Comparison is made with existing numerical techniques for dri...

Apparatus for determining motion vectors through the use of an adaptive median filtering technique

Abstract: An apparatus for determining motion vectors for pixels of a current frame which includes a number of processing blocks comprises a motion estimator for providing motion vectors for the pixels in the current frame, an edge detector for detecting edge positions of an object in the current frame to produce edge position signals, a median filter for providing filtered motion vectors of the pixels, a majority detector for obtaining a first and a second majority vectors corresponding to the pixels of a processing block, a controller for generating a control signal, a vector selection block for providing an output motion vector from the first and second major vectors which yields a motion compensated processing block having a least error function, and a switch responsive to the control signal for selecting a motion vector for each processing block from the output motion vector and a motion vector of the pixel located at the center of each processing block.

Adaptive filtering approaches for non-Gaussian stable processes

Abstract: A large class of physical phenomenon observed in practice exhibit non-Gaussian behavior. In this paper, /spl alpha/-stable distributions, which have heavier tails than Gaussian distribution, are considered to model non-Gaussian signals. Adaptive signal processing in the presence of such kind of noise is a requirement of many practical problems. Since, direct application of commonly used adaptation techniques fail in these applications, new approaches for adaptive filtering for /spl alpha/-stable random processes are introduced.

Filter bank-based processing of the stress ECG

Abstract: The algorithm presented is based on subband processing of the electrocardiogram (EGG), using Filter Banks (FB), and removes noise from stress-type ECGs. For Gaussian noise the FB-based method improves the signal-to-noise ratio (SNR), significantly better than the Mean and Median averaging methods. For muscle noise the FB-based method improves the SNR comparatively better than the Mean and Median averaging methods. The FB-based algorithm offers a way to process specific time periods in the heart beat cycle and remove noise in specific frequency bands.

Median filter behaviour with seismic data1

Abstract: Median filters may be used with seismic data to attenuate coherent wavefields. An example is the attenuation of the downgoing wavefield in VSP data processing. The filter is applied across the traces in the ‘direction’ of the wavefield. The final result is given by subtracting the filtered version of the record from the original record. This method of median filtering may be called ‘median filtering operated in subtraction’. The method may be extended by automatically estimating the slowness of coherent wavefields on a record. The filter is then applied in a time- and-space varying manner across the record on the basis of the slowness values at each point on the record. Median filters are non-linear and hence their behaviour is more difficult to determine than linear filters. However, there are a number of methods that may be used to analyse median filter behaviour: (1) pseudo-transfer functions to specific time series; (2) the response of median filters to simple seismic models; and (3) the response of median filters to steps that simulate terminating wavefields, such as faults on stacked data. These simple methods provide an intuitive insight into the behaviour of these filters, as well as providing a semiquantitative measurement of performance. The performance degradation of median filters in the presence of trace-to-trace variations in amplitude is shown to be similar to that of linear filters. The performance of median filters (in terms of signal distortion) applied obliquely across a record may be improved by low-pass filtering (in the t -dimension). The response of median filters to steps is shown to be affected by background noise levels. The distortion of steps introduced by median filters approaches the distortion of steps introduced by the corresponding linear filter for high levels of noise.

A Ring Median Filter for Digital Images

Abstract: The ring filter is defined as a median filter which assigns weight only to select pixels in an annulus. Its advantage is that it has a sharply-defined scale length; that is, all objects with a scale-size less than the radius of the ring are filtered and replaced by the local background level. It provides a fast, simple and intuitive method to remove the small-scale objects (independent of morphology) from a digital image, leaving behind the large-scale objects and overall light gradients. The ring filter is much faster than the more commonly used filled-box median filter, and completes in one or two passes what previously required a long iterative procedure. Several examples of its use are presented.

Interferogram phase noise reduction using morphological and modified median filters

Abstract: Phase unwrapping for SAR interferometry is one of the barriers for obtaining an operational interferometric SAR system (InSAR). For filtering the phase one uses mainly a low pass linear filter applied on the complex interferometric image before evaluating the phase. This paper compares three different non linear methods which filter the wrapped phase map. The first method is based on a morphological filter, the second one is a modified median filter and the third is a modified mode filter. All filters are built considering the periodic character of the wrapped phase function. The characteristics of these filtering approaches are compared, and the number of residues are calculated after each filtering step.

Mode filtering to reduce ultrasound speckle for feature extraction

Abstract: The authors investigate the use of filtering techniques to reduce speckle in ultrasound images, to improve their suitability for later feature extraction. The maximum likelihood estimator for a speckle corrupted image is shown to correspond to the statistical mode but this is difficult to determine for small populations, such as those contained by a filter mask. The truncated median filter approximates the mode by using the order of known image statistics and provides a fully automated image processing technique for speckle filtering. The filter's performance is established using a new quantitative evaluation scheme that closely considers the effect of filtering on edges, a key factor when applying features extraction in automated image interpretation. Application to in vivo and phantom test images shows that the truncated median filter provides clear images with strong edges, of quality exceeding that of other techniques. These benefits are confirmed by the application of feature extraction in arterial wall labelling

Two-dimensional rank-conditioned median filter

Abstract: A version of 2-D median filter specialized to impulse noise removal introducing negligible distortion in noise-free pixels is proposed Only pixels of prefixed ranks are replaced with the local median When extreme ranks (min and max) are chosen, spikes are selectively suppressed, more efficiently than by median filter Error probability for salt/pepper noise is theoretically derived MAE and visual comparisons with median filter attest increased accuracy >

Adaptive postprocessing algorithms for low bit rate video signals

Abstract: A new adaptive postprocessing algorithm to enhance the quality of a noisy video sequence is presented. The algorithm recognizes that the visibility of noise depends on local signal characteristics. It therefore classifies the video signal into different classes and uses separate nonlinear filters matched to each class. The most general version of the algorithm employs motion-compensated frame averaging to improve picture quality in a first stage. A classification algorithm subsequently divides subblocks of pixels in the averaged frame into four classes: edge, smooth, nonsmooth with motion and nonsmooth without motion. Spatial algorithms that perform multilevel median filtering, double median filtering, and median filtering are used for pixels belonging to edge, smooth, and nonsmooth with motion categories. Pixels in the nonsmooth, unmoving category are left unfiltered to preserve corresponding image texture. In a simpler version of this four-class system, the motion cues and motion-compensated frame averaging are eliminated, and the purely spatial filtering is based on a three-class algorithm. When used at the output of a 3-D subband coder at 384 kbps, the spatial postfilter was shown to provide a consistent gain in subjectively evaluated picture quality. Twenty-five viewers participated in an experiment involving three coded sequences. In a pairwise comparison of postfiltered and unfiltered sequences, the postfiltered version was judged to be better in 63 out of 75 instances. >

Real-time adaptive cancelling of ambient noise in lung sound measurement

Abstract: Ambient noise such as instrument noise and human voices often disturbs the hearing and/or measurement of lung sounds. Conventional frequency-domain filtering is usually ineffective. Noise is transmitted to the microphone that measures lung sounds through the chest wall around it, and it may be feasible to cancel out the noise by identifying this transfer function. The function, however, may vary with respect to the subject and measuring site, and therefore it should be modified dynamically. We apply an adaptive filtering technique to solve this problem. A workstation-based off-line adaptive noise canceller is developed to assess its performance in detail. Filter coefficients are controlled by a least-mean-square algorithm. Results show that the ambient noise is reduced by about 30 dB in a convergence time of several seconds. A real-time adaptive noise canceller is subsequently implemented by incorporating a digital signal processor, and a prototype electronic stethoscope is realised with high immunity to ambient noise. In a clinical application experiment in which the noise-contaminated lung souds are observed during an airway sensitivity test, satisfactory results are obtained. It is proved that the proposed method and device are effective for hearing and/or measuring lung sounds in noisy environments.