Showing papers on "Median filter published in 1986"

Stack filters

Abstract: The median and other rank-order operators possess two properties called the threshold decomposition and the stacking properties. The first is a limited superposition property which leads to a new architecture for these filters; the second is an ordering property which allows an efficient VLSI implementation of the threshold decomposition architecture. Motivated by the success of rank-order filters in a wide variety of applications and by the ease with which they can now be implemented, we consider in this paper a new class of filters called stack filters. They share the threshold decomposition and stacking properties of rank-order filters but are otherwise unconstrained. They are shown to form a very large class of easily implemented nonlinear filters which includes the rank-order operators as well as all compositions of morphological operators. The convergence properties of these filters are investigated using techniques similar to those used to determine root signal behavior of median filters. The results obtained include necessary conditions for a stack filter to preserve monotone regions or edges in signals. The output distribution for these filters is also found. All the stack filters of window width 3 are determined along with their convergence properties. Among these filters are found two which we have named asymmetric median filters. They share all the properties of median filters except that they remove impulses of one sign only; that is, one removes only positive going edges, the other removes only negative going edges, while the median filter removes impulses of both signs. This investigation of the properties of stack filters thus produces several new, useful, and easily implemented filters.

Nonlinear space-variant postprocessing of block coded images

Abstract: An important application of spatial filtering techniques is in the postprocessing of images degraded by coding. Linear, space-invariant filters are inadequate to reduce the noise produced by block coders. The noise in block coded images is correlated with the local characteristics of the signal, and such filters are unable to exploit this correlation to reduce the noise. We propose a new nonlinear, space-variant filtering algorithm which smooths jagged edges without blurring them, and smooths out abrupt intensity changes in monotone areas. Edge sharpness is preserved because near edges the filtering of the signal is negligible. Consequently, in-band noise is not reduced, but the well-known masking effect reduces the visibility of this in-band noise. The algorithm is only slightly more complex to implement than simple linear filtering. We present examples of processed images and SNR figures to demonstrate that a significant improvement in subjective and objective quality is achieved.

Median filter for reconstructing missing color samples

Abstract: Apparatus and method for sensing sampled colored image data and thereafter interpolating the sampled colored image data to provide image data in each color sampled for each point or pixel at which the subject is sensed from which an image of the subject may be constructed having reduced color artifacts and fringing while reducing the blurring to the image that would otherwise be required to correct for such artifacts and fringing.

Nonlinear mean filters in image processing

Abstract: The use of nonlinear means in image processing is introduced. The properties of these means in the presence of different types of noise are investigated. It is shown that nonlinear filters based on these means behave well for both additive and impulse noise. Their performance in the presence of signal dependent noise is satisfactory. They preserve the edges better than linear filters, and they reject the noise better than median filters.

Noise updating repeated Wiener filter and other adaptive noise smoothing filters using local image statistics.

Abstract: We consider the restoration of images degraded by a class of signal-uncorrelated noise, which is possibly signal-dependent. Some adaptive noise smoothing filters, which assume a nonstationary mean, nonstationary variance image model implicitly or explicitly, are reviewed, and their performances are compared by the mean-squares errors (MSES) and by the human subjective judgment. We also present a new noise smoothing technique which is called the noise updating repeated Wiener (NURW) filter. Explicit noise variance updating formulas are derived for the NURW filter. The performance is improved both in the MSE sense and in the vicinity of edges by subjective observation.

Adaptive median filter system

Abstract: An adaptive median filter system is disclosed. Circuitry is arranged to produce successive sets of samples from an input signal which may possibly include noise. An adaptive median filter filters the samples in response to a control signal. Further circuitry estimates the relative density of the noise in the input signal to generate the control signal supplied to the adaptive median filter.

Some convergence properties of median filters

Abstract: A median filter is a nonlinear digital filter which consists of a window of length 2N + 1 that moves over a signal of finite length. For each input sample, the corresponding output point is the median of all samples in the window centered on that input sample. Any finite length, M -level, signal that ends with constant regions of length N + 1 will converge to an invariant signal in a finite number of passes of this median filter. Such an invariant signal is called a root. The concept of a root signal has proved to be crucial in understanding the properties of the median filter, root signals are to median filters what passband signals are to linear signals. In this paper, two results concerning the rate at which a signal is filtered to a root are developed. For a window of width 3, we derive a recursive formula to count the number of binary signals of length L that converge to a root in exactly m passes of a median filter. Also, we show that, given a window of width 2N + 1 , any signal of length L will converge to a root in at most 3\lceil\frac{(L-2)}{2(N + 2)}\rceil passes of the filter.

A new algorithm for real-time median filtering

Abstract: An algorithm is presented to determine the median of a given set of samples W. The median is determined by making r comparisons of the elements of the "m-array," where r is the number of bits in the radix-2 representation of the largest element of W. The m-array has 2relements, and m k equals the number of samples of W greater than or equal to (k - 1). The method is suitable to determine the running median in median filtering since updating the m-array is a very simple task.

A Nonlinear Filter to Remove Impulse Noise from Meteorological Data

Abstract: Nonlinear median filters were modified to use threshold logic and used to remove impulse noise (spikes) from a set of meteorological data. The impulse noise in the dataset, which originated in the communications section of the Portable Automated Mesonet, could be characterized as random bit noise. Most of the pulses had a duration of one time interval, which in this case was one minute. The filters were effective irrespective of the frequency of occurrence and of the amplitude of the noise spikes. Pulses were removed even when the frequency of occurrence rose to every other data point as was observed in several short intervals. The amplitude of pulses removed ranged over three orders of magnitude.

An adaptive comb filtering method as applied to acoustic analyses of pathological voice

Abstract: An adaptive comb filtering method, which was initially investigated by Frazier et al. [5] for the enhancement of degraded speech due to additive noise, is applied for estimating vocal noise generated at the glottis due to pathological changes of the vocal folds. In applying the method, special emphasis is placed on the accurate determination of pitch period durations. The amount of estimated vocal noise is quantified by a novel acoustic measure, normalized noise energy(NNE). Experiments with vowel samples spoken by 64 normal control subjects and 50 patients with glottic cancer have shown that the NNE is useful for the distinction between normal and pathological voice status.

Statistical threshold decomposition for recursive and nonrecursive median filters

Abstract: The statistical analysis of recursive nonlinear filters is generally difficult. The analysis of recursive median filters has been limited to the trivial cases of signals with a small number of quantization levels and to small window sizes. A block state description of recursively filtered signals is developed, and by applying this description to threshold decomposition, closed-form expressions for the statistics of recursive median filters are obtained. In this case, the number of quantization levels and the window size do not increase the analysis complexity since the output statistics depend on the distribution of a single-threshold filtered binary signal. The statistical decomposition is also developed for nonrecursive median filter operations yielding a connection from classical order statistics to the threshold decomposition approach. Finally, some statistical properties are derived for recursively median-filtered signals.

Morphological Transformations By Hybrid Optical-Electronic Methods

Abstract: Morphological shape transformations on binary imagery can be performed by optically convolving the input image with the desired structuring element (e.g., a disk) and thresholding the output. Photographic film can be used for thresholding, but better results are obtained with electronic scanning and hard-limiting. Erosions, dilations, and median filtering can be performed directly. Openings and closings require an image store and feedback. Results of simple experiments are presented.

The analog median filter

Abstract: Discrete median filters are a special class of ranked-order digital filters used for smoothing signals. In this paper, the analog median filter is defined and proposed for analysis of the standard discrete median filter in cases with a large sample size or when the associated statistics would be simpler in the continuum. Discrete filters are shown to be a subclass of analog filters. Also, an equivalence among analog filters and limits of discrete filters is established. Finally, several stochastic interpretations of the analog median filter are presented including necessary and sufficient conditions on input processes which guarantee the existence of output distributions for multiple passes of the analog median filter

An application of median filters to digital television

Abstract: The properties of the median filter can be used to great advantage in the improvement of television images corrupted with "sparklie" noise. In this paper we describe the application of the median filter to a five second noisy movie sequence received from a satellite antenna with a weak signal. The sequence of two dimensional TV images is decomposed into a set of one dimensional signals. Each one dimensional signal consists of the succession of values a fixed picture element (pixel) takes as successive frames arrive for display. The median filter is applied separately to each of these one dimensional signals. It is shown that the portion of TV frames not containing noise almost always appears as a root signal to the median filter. Finally a real time implementation of the filtering scheme is proposed.

Median filter for reducing data error in distance measuring equipment

Abstract: A set containing an odd number N of successive raw range data samples from precision distance measuring equipment is established. The set is arranged in ascending order of sample magnitude. The median member of the ordered set, i.e. the (N-1/2+1 member, is selected for processing in an alpha-beta type digital filter. The filter output is compensated for the delay introduced by using the median member of the set as the input to the alpha-beta filter. The compensated filter output is utilized as the distance input signal to a display or aircraft flight control system.

Apparatus and methods for speech analysis

Abstract: Input signals representative of speech are unreliable as inputs for speech recognition if processed conventionally by, among other processes, filtering into separate frequency bands. Further processing according to the invention takes the output from a filter bank and after operations of rectification and integration provides a process of median filtering and smoothing which significantly reduces the sampling rate of the filtered signals while retaining the important acoustic features of the input speech.

Iterative enhancement of noncoherent radar data

Abstract: In this paper, the application of a constrained iterative deconvolution algorithm to the problem of enhancing noncoherent radar data is described. It is shown that noncoherent reconstruction is theoretically not unique. Despite this, an approximate convolutional model of the observed data is established and it is seen that reasonable results are observed. A key feature of the algorithm is the use of a median filter and threshold to segment the data into two classes, potential point targets and background, which are deconvolved separately subject to different nonlinear constraints. The performance of the algorithm is demonstrated using real air-to-ground millimeter-wave noncoherent radar data.

Interference detection and correction applied to incoherent scatter radar power profile measurements

Abstract: A three-stage interference detection and correction data processing scheme has been applied to Arecibo D region incoherent scatter power spectra. Individual power spectral estimates at a particular frequency are, in the absence of interference, Gaussian distributed about the mean for that frequency. The first processing stage involves rank ordering or median filtering techniques to estimate the parameters of the underlying Gaussian process at each spectral frequency. The second processing stage uses this information to construct a “window” which excludes the upper and lower 1% of the Gaussian distributed (interference free) power spectral data. Since interference power is additive, this window also excludes any interference contaminated spectral points that might also be present. The final processing stage involves weighted “fitting” of a model spectrum to the final corrected and averaged spectra. This three-stage signal processing scheme has yielded significant improvements in final data quality.

Method for restoring the background appearance of two-dimensional filtered seismic data

Abstract: A method for restoring to filtered seismic data at least some of the ramdom background noise associated with the data in its form prior to filtering The method includes the steps of filtering seismic data in a two-dimensional filter; generating a noise signal representing the random background noise associated with the data; filtering the noise signal in an inverse filter corresponding to the two-dimensional filter; and adding the inverse-filtered first signal to the filtered seismic data In one embodiment, the noise signal is generated by filtering a copy of the data in a least-mean-squares adaptive filter to remove substantially all coherent energy therefrom In another embodiment, the noise signal is an independently generated white noise signal having beginning and end times matching those of the seismic data This white noise signal is the bandpass filtered to cause its frequency content to match that of the seismic data Examples of two-dimensional filters that may be employed include a dip filter, an effective filter associated with migration, or a filter used in running mix operations Data processed in accordance with the invention have a completely filtered coherent portion and a restored background noise portion, so that the processed data, when displayed, exhibit greater reflector continuity, more accurate amplitude relationships, and better cosmetic acceptability

Enhancement of noisy speech by forward/Backward adaptive digital filtering

Abstract: In this paper, a forward/backward adaptive digital filtering method is studied for the enhancement of noisy speech. The enhancing algorithm uses future samples as well as previous samples to estimate a current sample, and utilizes the correlational property of speech. It is effective for the enhancement of narrow-band as well as wide-band noisy speech. To improve the enhancing capability further, its modified version is also considered. This modified algorithm, of which structure is relatively simple, works for any kind of additive noise, and results in signal-to-noise ratio gain over existing enhancement algorithms by about 2 dB.

3 - An algorithm for filtering multiplicative noise in wide range

Abstract: A new algorithm for filtering of multiplicative noise in image processing is considered . A triangular function is used to approximate the local probability density function of the image . Under this assumption the computation of necessary statistics is greatly simplified . An effective filtering algorithm for processing multiplicative noise is then developed. Experimental results show that the algorithm is effective in processing both small and large multiplicative noises. Also the computation and storage requirements are reasonable to implement in a minicomputer.

A robust adaptive filter for noise reduction problems

Abstract: Classical optimum adaptive filtering assumes that some second order moments are known, so that the optimality is linked to the accurate knowledge of the letter moments. In most practical cases, this knowledge is uncertain, estimates must be utilized instead of true values, and a considerable decrease in the performance is often deplored. Thus, two related problems arise : first, a single optimization criterion must be clearly defined to provide a joint estimate of the unknown variables and the second order moments; secondly, a measure of the estimated filter performance should be used to enquire into the optimality of the estimated solution.

Estimation Of Ship Profiles From A Time Sequence Of Forward-Looking Infrared Images

Abstract: This paper addresses the problem of estimating the profile, or silhouette, of a ship target from a sequence of forward-looking infrared (FLIR) imagery obtained at video rates. We are specifically interested in the performance of multiframe processing techniques compared with single-frame methods. Our approach to multiframe signal processing combines spatial and temporal processing in two stages: first, a target profile is extracted from each image frame by a segmentation algorithm; second, the resulting sequence of profiles is temporally filtered in order to increase the signal-to-noise ratio. Long sequences of FLIR imagery tend to exhibit several characteristic forms of non-Gaussian noise (random speckle, occlusions, and flaring) caused by various atmospheric and background phenomena, as well as instrument noise. Simple temporal averaging is inadequate in this environment. In this paper we develop a combination of spatial and temporal processing algorithms that may be used to overcome the signal-to-noise ratio problems associated with these noise effects. A recursive temporal median filter in conjunction with a simple spatial segmentation algorithm is proposed for this purpose. Experimental results based on a database of FLIR ship imagery are presented in support of the spatiotemporal processor. Although the approach is developed for ship targets, we feel it could be adapted for a broader class of FLIR applications.

2-D Multilevel FIR-median hybrid filters

Abstract: A new class of median type filters for image processing is proposed. In the filters linear FIR substructures are used in conjunction with the median operation. The root signals and noise attenuation properties of the FIR-median hybrid (FMH) and multilevel FMH filters are compared with representative edge preserving filtering operations. The concept of multilevel median operation is introduced to improve the detail preserving property of conventional median and the FMH filters. In the multilevel filters there exists a trade-off between noise attenuation and detail preservation. The analysis and examples indicate that FMH filters preserve details better and are computationally much more efficient than the conventional median and the K-nearest neighbor averaging filters.

A CMOS Stereo 16 Bit D/A Converter for Digital Audio

Abstract: A stereo D/A converter for digital audio applications is presented which obtains 16 bit resolution from a one bit converter, using a code conversion technique based upon oversampling and noise shaping. Linear-phase low-pass filtering is implemented in the code converter to allow simple analog output filtering.

Fast Computing Median Filters On General-Purpose Image Processing Systems

Abstract: Separable median filters over 3 X3 neighborhoods can be realized by applying in succession logical operations in the horizontal and vertical directions. We show how to take advantage of the architectural resources of commercially available pipelined image processing systems in implementing these filters. The required logical operations are decomposed into a succession of image additions, subtractions, and translations that can be executed very efficiently. The proposed method showed a 70-fold improvement of processing speed over the well-known histogram updating method for 3 X3 filters over 512 X512 X8 bit images.

Some Examples of IP Algorithms for Pyramidal Architecture

Abstract: Aim of the paper is to present a class of parallel algorithms for Image Processing (IP) implemented in a pyramid machine architecture. The complexity of the algorithms and their performance as regards to a serial processor is also given.

Novel structure for television picture noise reduction

Abstract: A new method for noise reduction in image sequences is described which is based on a modified form of the well known first-order recursive temporal filter The new features of the system are the improved separation of signal and noise and subsequently the reduction of noise in moving areas This is accomplished without increasing the complexity of the whole system very much