Showing papers on "Median filter published in 1996"

A new efficient approach for the removal of impulse noise from highly corrupted images

Abstract: A new framework for removing impulse noise from images is presented in which the nature of the filtering operation is conditioned on a state variable defined as the output of a classifier that operates on the differences between the input pixel and the remaining rank-ordered pixels in a sliding window. As part of this framework, several algorithms are examined, each of which is applicable to fixed and random-valued impulse noise models. First, a simple two-state approach is described in which the algorithm switches between the output of an identity filter and a rank-ordered mean (ROM) filter. The technique achieves an excellent tradeoff between noise suppression and detail preservation with little increase in computational complexity over the simple median filter. For a small additional cost in memory, this simple strategy is easily generalized into a multistate approach using weighted combinations of the identity and ROM filter in which the weighting coefficients can be optimized using image training data. Extensive simulations indicate that these methods perform significantly better in terms of noise suppression and detail preservation than a number of existing nonlinear techniques with as much as 40% impulse noise corruption. Moreover, the method can effectively restore images corrupted with Gaussian noise and mixed Gaussian and impulse noise. Finally, the method is shown to be extremely robust with respect to the training data and the percentage of impulse noise.

Weighted median filters: a tutorial

Abstract: Weighted Median (WM) filters have the robustness and edge preserving capability of the classical median filter and resemble linear FIR filters in certain properties. Furthermore, WM filters belong to the broad class of nonlinear filters called stack filters. This enables the use of the tools developed for the latter class in characterizing and analyzing the behavior and properties of WM filters, e.g. noise attenuation capability. The fact that WM filters are threshold functions allows the use of neural network training methods to obtain adaptive WM filters. In this tutorial paper we trace the development of the theory of WM filtering from its beginnings in the median filter to the recently developed theory of optimal weighted median filtering. Applications discussed include: idempotent weighted median filters for speech processing, adaptive weighted median and optimal weighted median filters for image and image sequence restoration, weighted medians as robust predictors in DPCM coding and Quincunx coding, and weighted median filters in scan rate conversion in normal TV and HDTV systems.

Advanced Signal Processing and Digital Noise Reduction

Abstract: Introduction to Signal Processing and Noise Reduction Stochastic Processes and Statistical Characterization of Signals Signal Transforms Bayesian Probabilistic Estimation Theory Wiener Filters and Kalman Filters Linear Prediction Models Sample-Adaptive Least Squared Error Filters Power Spectrum Estimation Finite-State Statistical Models for Non-stationary Stochastic Processes Interpolation of a Sequence of Samples Modelling, Detection and Removal of Impulsive Noise Spectral Subtraction Removal of Transient Noise Pulses Echo Cancellation and Multi-Input Adaptive Noise Reduction Adaptive Notch Filters Channel Equalization Noise Compensation for Speech Recognition in Adverse Environments.

Combining frequency and spatial domain information for fast interactive image noise removal

Abstract: Scratches on old films must be removed since these are more noticeable on higher definition and digital televisions. Wires that suspend actors or cars must be carefully erased during post production of special effects shots. Both of these are time consuming tasks but can be addressed by the following image restoration process: given the locations of noisy pixels to be replaced and a prototype image, restore those noisy pixels in a natural way. We call it image noise removal and this paper describes its fast iterative algorithm. Most existing algorithms for removing image noise use either frequency domain information (e.g low pass filtering) or spatial domain information (e.g median filtering or stochastic texture generation). The few that do combine the two domains place the limitation that the image be band limited and the band limits be known. Our algorithm works in both spatial and frequency domains without placing the limitations about band limits, making it possible to fully exploit advantages from each domain. While global features and large textures are captured in frequency domain, local continuity and sharpness are maintained in spatial domain. With a judicious choice of operations and domains in which they work, our dual-domain approach can reconstruct many contiguous noisy pixels in areas with large patterns while maintaining continuity of features such as lines. In addition, the image intensity does not have to be uniform. These are significant advantages over existing algorithms. Our algorithm is based on a general framework of projection onto convex sets (POCS). Any image analysis technique that can be described as a closed convex set can be cleanly plugged into the iteration loop of our algorithm. This is another important advantage of our algorithm. CR Categories: I.3.3 [Computer Graphics]: Picture / Image Generation; Display Algorithms; I.3.6 [Computer Graphics]: Methodology and Techniques – Interaction techniques; I.4.4 [Image Processing]: Restoration; I.4.9 [Image Processing]: Applications. Additional

A comprehensive evaluation of filters for radar speckle suppression

Abstract: This paper presents the result of a comprehensive evaluation of filters for radar speckle suppression. Seven filters in the Radar Module of Erdas/IMAGINE(R) were evaluated, including the mean filter, the median filter, the Lee-sigma filter, the local region filter, the Lee filter, the Frost filter, and the MAP (maximum a posteriori) filter. The performance of these filters was measured in terms of five criteria: speckle suppression index, edge enhancing index, feature preserving index (for both linear features and point features), image detail preserving coefficient, and speckle image analysis. Visual effect of filtered image and its filter theoretical basis were discussed. The relationship between filter performance and speckle patterns was also examined.

Video preprocessing method and apparatus with selective filtering based on motion detection

Abstract: Video preprocessing methods and apparatus which utilize motion detection to control selective filtering of pels in a sequence of video frames wherein a video preprocessor generates first and second motion metrics for a given pel in a current frame N by taking the difference between the given pel and corresponding pels in a previous frame N-1 and a subsequent frame N+1, respectively. The motion metrics are converted to first and second bitmaps by thresholding the motion metric value against a first threshold. A motion detection metric is then generated for a given pel by summing the values of the first and second bitmaps for a group of pels which includes the given pel, and comparing the result to a predetermined threshold. The group of pels may include five pels on each of two lines above the given pel, and five pels on each of two lines below a given pel. The motion detection metric may be used to determine whether or not temporal median filtering and/or temporal lowpass filtering operations should be applied to the given pel. The motion detection metric may also be used in conjunction with an edge detection metric and a frame-wise motion activity measure to generate an address into a look-up table. The look-up table specifies a set of filter coefficients for use in a spatial lowpass filtering operation applied to the given pel.

Method and system for the detection of lesions in medical images

Abstract: A method and system for the automated detection of lesions in the medical images. Medical images, such as mammograms are segmented and optionally processing with peripheral enhancement and/or modified median filtering. A modified morphological open operation (104-106) and filtering with a modified mass filter (107-109) are performed for the initial detection of circumscribed lesions. Then, the lesions are matched using a deformable shape template with Fourier descriptors (110-112). Characterization of the match is done using simulated annealing, and measuring the circularity and density characteristics of the suspected lesion. The procedure is performed iteratively at different spatial resolution in which at each resolution step a specific lesion size is detected. The detection of the lesion leads to a localization of a suspicious region and thus the likelyhood of cancer.

Process for interpolating progressive frames

Abstract: A process for converting interlaced frames into progressive frames comprising a change of frame frequency by interpolation and motion compensation, wherein when a motion vector associated with a pixel to be interpolated is non-zero or when the motion vector is zero but the confidence accorded to this vector is less than a given threshold, the interpolation of the pixel of a frame situated temporally between two input frames is carried out by a median filtering pertaining to the values obtained by a first motion compensated linear temporal filter, a second motion compensated linear filter, and a motion compensated median temporal filter.

The rational filter for image smoothing

Abstract: A nonlinear operator is presented that is able to effectively attenuate the noise that corrupts an image while introducing small distortions on the image details. It is described by a rational function, i.e., by the ratio of two polynomials in the input variables. Notwithstanding its simplicity, this operator proves to be more powerful than conventional methods for many noise distributions.

Image decoder using adjustable filtering

Abstract: Noise contained in a reproducing image signal is suppressed by performing a filtering operation on an image signal obtained by decoding data that has been coded with a unit of a block consisting of m×n pixels. A filter circuit having a plurality of filter characteristics suppresses noise, and a characteristics selection circuit switches the filter characteristics of the filter circuit by use of a quantizing parameter employed for coding the image signal.

An improved recursive median filtering scheme for image processing

Abstract: In a recent publication, it was shown that median filtering is an optimization process in which a two-term cost function is minimized. Based on this functional optimization property of the median filtering process, a new approach for designing the recursive median filter for image processing applications is introduced in this paper. We prove that the new approach is guaranteed to converge to root within a finite number of iterations. The new method is applied to process a real image corrupted by pseudorandom impulsive noise, and the results show that the new scheme provides improved mean square error (RISE) performance over the standard recursive median filters.

Adaptive noise removal from biomedical signals using warped polynomials

Abstract: Presents the time-warped polynomial filter (TWPF), a new interval-adaptive filter for removing stationary noise from nonstationary biomedical signals. The filter fits warped polynomials to large segments of such signals. This can be interpreted as low-pass filtering with a time-varying cutoff frequency. In optimal operation, the filter's cut-off frequency equals the local signal bandwidth. However, the author also presents an iterative filter adaptation algorithm, which does not rely on the (complicated) computation of the local bandwidth. The TWPF has some important advantages over existing adaptive noise removal techniques: it reacts immediately to changes in the signal's properties, independently of the desired noise reduction; it does not require a reference signal and can be applied to nonperiodical signals. In case of quasiperiodical signals, applying the TWPF to the individual signal periods leads to an optimal noise reduction. However, the TWPF can also be applied to intervals of fixed size, at the expense of a slightly lower noise reduction. This is the way nonquasiperiodical signals are filtered. The author presents experimental results which demonstrate the usefulness of the interval-adaptive filter in several biomedical applications: noise removal from ECG, respiratory and blood pressure signals, and base-line restoration of electroencephalograms (EEGs).

Image noise reduction system using a wiener variant filter in a pyramid image representation

Abstract: The invention relates to a novel process and system for removing noise from an image by first noise modeling an image signal source to generate noise masks and LUT values characteristic of noise at different frequency levels for each channel, and then applying the stored noise masks and LUT values to an image signal for noise removal. The image is first captured as an electronic image signal by the image signal source, then represented by a pyramid structure whereby each successive level of the pyramid is constructed from DC values of the previous level, and each level of the pyramid corresponds to a different frequency band of the image signal. A Wiener variant filter using DCT transforms is used to filter DCT coefficients at each level. The image is restored with reduced noise by replacing DC values with next level IDCT coefficients then performing an IDCT on the results.

Noise filtering in solar speckle masking reconstructions

Abstract: A new method of obtaining a sensitive noise filter for solar speckle masking reconstructions is presented below. This filter separates the true image information from noise most reliably. Its efficiency is demonstrated by some representative examples considering observed and artificial image data which were generated in a computer. The latter set of data also suffered realistic degradations by the influence of seeing and noise taken from suitable observations.

A feature tracking algorithm using neighborhood relaxation with multi-candidate pre-screening

Abstract: Tracking of features in video sequences has many applications. Conventionally, the minimum displaced frame difference (referred to as DFD or residue) of a block of pixels is used as the criterion for tracking in block-matching algorithms (BMA). However, such a criterion often misses the true motion vectors, due to many practical factors, e.g. affine warping, image noise, object occlusion, lighting variation, and existence of multiple minimal DFD. Our goal is to find motion vectors of the features for object-based motion tracking, in which (1) any region of an object contains a good number of blocks, whose motion vectors exhibit certain consistency; and (2) only true motion vectors for a few blocks per region are needed. Hence, we propose a new tracking method. (1) At the outset, we disqualify some of the reference blocks which are considered to be unreliable to track. (2) We adopt a multi-candidate pre-screening to provide some robustness in selecting motion candidates. (3) Assuming the true motion field is piecewise continuous, we determine the motion of a feature block by consulting all its neighboring blocks' directions. This allows for the chance that a singular and erroneous motion vector may be corrected by its surrounding motion vectors (just like median filtering). Our method is also designed for tracking more flexible affine-type motions, such as rotation, zooming, sheering, etc. Finally, the performance improvement over other existing methods is demonstrated.

Image processing for noise reduction

Abstract: An image processing method includes multi-resolution decomposition to decompose an input image into frequency-band images, which are subsequently filtered according to an order statistics filtering. Preferably, a finite impulse response median hybrid is employed. The filtered frequency-band images are synthesized to form the filtered output image.

Noise estimation and reduction apparatus for video signal processing

Abstract: Video signal compression apparatus provides a means for generating residues (12,16,17,18,14) representing pixel value differences between predicted and real pixel values of a current frame of a video signal. Noise reduction circuitry, in the form of nonlinear processing means (500), attenuates lower amplitude residues greater than higher amplitude residues and is responsive to a noise estimate (1333). The processed residues are transformed (15) to provide a compressed video data output. Disclosed nonlinear processing functions attenuate noise and reduce image distortion.

Recursive median filters of increasing order: a variational approach

Abstract: For a certain class of signals, it is known that the recursive median filters provide a minimum of a related regularizing functional, defined by means of the l/sub 1/ norm. We generalize such results, and we show that a good approximation to the minima of such a functional can be obtained, for any signal, by means of successive applications of recursive median filters of increasing order.

Genetic algorithm implementation of stack filter design for image restoration

Abstract: Stack filters are a class of nonlinear spatial operators used for noise suppression. Their design is formulated as an optimisation problem and genetic algorithms (GAs) are used to perform the configuration. Applying the mean absolute error (MAE) as the basis of an objective function, the stack filter is used to restore magnetic resonance (MR) images corrupted with uncorrelated additive noise from 10% and 50%. The filter is trained on corresponding patches of the original and noisy image and then applied to the whole image. The outcomes are compared with the median filter and return a smaller MAE for all noise levels. The dependency of MAE on training window size and GA early termination is examined, showing that a reduction of 75% in computational complexity can be achieved by a 10% relaxation in MAE. The design is then extended from 9-point to 13-point filters and by training on Poisson noise, the filter is applied to nuclear medicine bone scans where no absolute truth exists. Surface topology, image profiles and the measurement of relative contrast show its value in reducing noise whilst preserving contrast. Because of its computational complexity the process has been implemented as a distributed GA using the parallel virtual machine (PVM) software.

Interframe coding of magnetic resonance images

Abstract: Presents a new interframe coding method for medical images, in particular magnetic resonance (MR) images. Until now, attempts in using interframe redundancies for coding MR images have been unsuccessful. The authors believe that the main reason for this is twofold: unsuitable interframe estimation models and the thermal noise inherent in magnetic resonance imaging (MRI). The interframe model used here is a continuous affine mapping based on (and optimized by) deforming triangles. The inherent noise of MRI is dealt with by using a median filter within the estimation loop. The residue frames are quantized with a zero-tree wavelet coder, which includes arithmetic entropy coding. This particular method of quantization allows for progressive transmission, which aside from avoiding buffer control problems is very attractive in medical imaging applications.

System design for pixel-parallel image processing

Abstract: A system design for performing low-level image processing tasks in real time is presented. The design is based on large processor-per-pixel arrays implemented using integrated circuit technology. Two integrated circuit architectures are summarized: an associative parallel processor and a parallel processor employing DRAM cells. In both architectures, the layout pitch of one-bit-wide logic is matched to the pitch of memory cells to form high-density processing element arrays. The system design features an efficient control path implementation, providing high processing element array utilization without demanding complex controller hardware. Sequences of array instructions are generated by a host computer before processing begins, then stored in a simple controller. Once processing begins, the host computer initiates stored sequences to perform pixel-parallel operations. A programming framework implemented using the C++ programming language supports application development. A prototype system employs associative parallel processor devices, a controller, and the programming framework. Three sample applications, smoothing and segmentation, median filtering, and optical flow, establish the suitability of the system for real-time image processing.

VLSI implementation of a selective median filter

Abstract: The VLSI implementation of a selective median filter for real-time applications is presented. The proposed design is based on a novel bit-level running algorithm with a modular and parallel structure. A chip designed by the cell-based style is demonstrated to show the hardware realization. The performance of the proposed design is also presented.

Multiscale recursive medians, scale-space, and transforms with applications to image processing

Abstract: A cascade of increasing scale, 1-D, recursive median filters produces a sieve, termed an R-sieve, has a number of properties important to image processing. In particular, it (1) Simplifies signals without introducing new extrema or edges, that is, it preserves scale-space. It shares this property with Gaussian filters, but has the advantage of being significantly more robust. (2) The differences between successive stages of the sieve yield a transform, to the granularity domain. Patterns and shapes can be recognized in this domain using idempotent matched sieves and the result transformed back to the spatial domain. The R-sieve is very fast to compute and has a close relationship to 1-D alternating sequential filters with flat structuring elements. They are useful for machine vision applications.

Nonlinear separation of crackles and squawks from vesicular sounds using third-order statistics

Abstract: A combination of nonlinear filtering with third-order statistics is presented, resulting to a modification of the nonlinear digital ST-NST filter, proposed by Arakrwa et al. (1986), for improving its performance in noisy environments. The use of third-order statistics in estimating the AR-model's order and coefficients provides more reliable estimates of the stationary part of the input signal. The implementation of this modified ST-NST filter in fine crackles, coarse crackles and squawks was examined. Experiments have shown that a reliable and accurate separation of these adventitious sounds from vesicular sounds can be achieved, using this method, even in the presence of additive Gaussian noise.

Digital adaptive robust algorithms for radar image filtering

Abstract: Novel adaptive robust filtering algorithms applicable to radar image processing are proposed. They take into consideration the peculiarities of radar images and possess a good combination of properties: effective speckle suppression, impulsive noise removal, edge and detail preservation and low computational complexity. The advantages of these digital algorithms are demonstrated by simulated data and images obtained by airborne side-look non SAR radar.

Television noise reduction ic

Abstract: A noise reduction IC for consumer television has been designed. The IC contains a spatial filter for Gaussian noise and a temporal filter for clamp noise. To reduce clamp noise, the average value of the pixels in a line-segment are filtered rather than individual pixels. This reduces the cost of the temporal filter significantly, enabling the use of embedded memory. Analog interfaces are provided, as well as a line-locked clock generator. The chip includes a noise level estimator for optimal filtering under varying reception conditions. The average gain of the spatial filter is around 3 dB, whereas the temporal filter yields up to 8 dB improvement on clamp noise.