Showing papers on "Median filter published in 1994"

Three-dimensional median-related filters for color image sequence filtering

Abstract: Most current algorithms developed for image sequence filtering require motion information in order to obtain good results both in the still and moving parts of an image sequence. In the present paper, filters which completely preserve stationary regions in image sequences are introduced. In moving regions, the 3D filters inherently reduce to spatial filters and perform well in these areas without any motion-compensation or motion-detection. A new multivariate filtering operation called the alpha-trimmed vector median is proposed. Guidelines for the determination of optimal 3D median-related structures for color and gray-level image sequence filtering are given. Algorithms based on vector median, extended vector median, alpha-trimmed vector median, and componentwise median operations are developed. Properties of the human visual system are taken into account in the design of filters. Noise attenuation and detail preservation capability of the filters is examined. In particular, the impulsive noise attenuation capability of the filters is analyzed theoretically. Simulation results based on real image sequences are given. >

Decision-based median filter using local signal statistics

Abstract: Noise removal is important in many applications. When the noise has impulsive characteristics, linear techniquesdo not perform well, and median filter or its derivatives are often used. Although median-based filters preserve edgesreasonably well, they tend to remove some of the finer details in the image. Switching schemes — where the filter isswitched between two or more filters — have been proposed, but they usually lack a decision rule efficient enough toyield good results on different regions of the image. In this paper we present a strategy to overcome this problem. Adecision rule based on the second order local statistics of the signal (within a window) is used to switch between theidentity filter and a median filter. The results on a test image show an improvement of around 4dB over the medianfilter alone, and 2dB over other techniques.Keywords: Median filter; Image enhancement; Noise removal; Impulsive noise. 1. INTRODUCTION Noise reduction is often necessary as a pre-processing step in situations where a signal is contaminated by noise.In cases where the noise can be adequately modeled as additive Gaussian noise, linear filters are normally efficiciitfor noise-reduction. However, in many cases the noise is impulsive, and in this case linear techniques do not usuallyperform well. The median filter and its derivatives are often the filter of choice for these applications.The median filter is a non-linear filter, and it has the useful property of removing (reducing) impulsive noisewithout (severely) smoothing the edges of the signal. The main drawback of the median filter is that it also modifiesthe points not contaminated by noise, therefore removing the finer details in the signal.In the past 20 years, median filters have been generalized and modified in many ways. A good overview of pastwork on generalizations of median filters can be find in the paper by Gabbouj et al.1 Examples include rank orderfilters, weighted median filters, stack filters, and linear combinations of nonlinear filters. A theory for optimal stackfilters has been developed.2 More recently, filters where the rank selected is based on the pixel rank have been alsoproposed .

Speckle noise filtering in ultrasound imaging

Abstract: The enhancement of ultrasound images is provided through the filtering of signal dependent noise such as speckle noise by dividing the signal into selective subintervals and utilizing discrete wavelet transform and the identification and selection of those wavelet transform coefficients primarily including signal and not those primarily including signal dependent noise.

Tree-structured nonlinear filters in digital mammography

Abstract: A new class of nonlinear filters with more robust characteristics for noise suppression and detail preservation is proposed for processing digital mammographic images. The new algorithm consists of two major filtering blocks: (a) a multistage tree-structured filter for image enhancement that uses central weighted median filters as basic sub-filtering blocks and (b) a dispersion edge detector. The design of the algorithm also included the use of linear and curved windows to determine whether variable shape windowing could improve detail preservation. First, the noise-suppressing properties of the tree-structured filter were compared to single filters, namely the median and the central weighted median with conventional square and variable shape adaptive windows; simulated images were used for this purpose. Second, the edge detection properties of the tree-structured filter cascaded with the dispersion edge detector were compared to the performance of the dispersion edge detector alone, the Sobel operator, and the single median filter cascaded with the dispersion edge detector. Selected mammographic images with representative biopsy-proven malignancies were processed with all methods and the results were visually evaluated by an expert mammographer. In all applications, the proposed filter suggested better detail preservation, noise suppression, and edge detection than all other approaches and it may prove to be a useful tool for computer-assisted diagnosis in digital mammography. >

Image processing method and apparatus.

Abstract: Disclosed is an edge sharpening method using maximum and minimum filters. In an embodiment of the present invention, the edge region of an input image signal is detected, minimum and maximum signals are generated for the detected edge region, the input signal of the detected edge region is compared with the average signal of the minimum and maximum signals, and the average of the input signal and the maximum signal is output if the input signal is larger than the average signal, the average of the input signal and the minimum signal is output if the input signal is smaller than the average signal, and the input signal is output if the input signal is equal to the average signal. Meanwhile, median filtering, using a window perpendicular to the direction in which the edge sharpening is performed, is performed on the region on which the edge sharpening has been performed.

Adaptive filtering for non-Gaussian stable processes

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

Near-sensor image processing: a new paradigm

Abstract: The paper introduces the concept of near-sensor image processing. By this, the authors mean techniques in which the physical properties of the image sensor itself is utilized to do part of the signal processing task. It is shown that the analog-temporal behavior of photodiodes combined with thresholding amplifiers can be used favorably to do certain low-level image processing tasks including median filtering and convolution. The given examples also show how adaptivity to different light levels can be achieved in a natural way. To extract features from the image, such as moments and shape factors, the authors introduce a simple measurement function. >

Fast vector median filter based on Euclidean norm approximation

Abstract: The vector median filter has good filtering capabilities; nevertheless, its huge computational complexity significantly limits its practical usability. A vector median filter based on a fast approximation of the Euclidean norm is presented. The proposed algorithm couples computational and filtering effectiveness, and it is well suited for hardware implementation. Theoretical and experimental results regarding both approximation error and speed improvement prove the validity of the proposed algorithm. >

Fuzzy filtering for mixed noise removal during image processing

Abstract: Presents a new nonlinear fuzzy filter for image processing in a mixed noise environment, where both additive Gaussian noise and nonadditive impulsive noise may be present. Averaging filters can effectively remove the Gaussian noise and order statistics filters or median filters can effectively remove the impulsive noise. However, it is difficult to combine these filters to remove mixed noise in an image processing environment without blurring the image details or edges. Trying to distinguish between noise and edge information in the image is an inherently ambiguous problem and naturally leads to the development of a fuzzy filter. We use local statistics to train the membership function of a fuzzy filter for image processing to remove both Gaussian noise and impulsive noise while preserving edges. We show that such a fuzzy filter gives superior results when compared to averaging filters, median filters, and other fuzzy filters. We also demonstrate the robustness of this filtering technique. >

adaptive fourier threshold filtering: A method to reduce noise and incoherent artifacts in high resolution cardiac images

Abstract: We introduce an image processing method which reduces white noise and random artifacts in sets of high resolution, time resolved images. At each pixel, the processing consists of: 1) the isolation of a time intensity curve (TIC), 2) Fourier transformation of each TIC, 3) application of a threshold to remove low intensity coefficients, 4) inverse transformation to generate noise reduced TICs which are recombined to form images with improved signal-to-noise ratio (SNR). Noise filtering by Fourier thresholding is demonstrated on a set of cardiac images, resulting in a reduction of the noise energy by approximately 90%.

Reduction of noise in AVHRR channel 3 data with minimum distortion

Abstract: The channel 3 data of the Advanced Very High Resolution Radiometer (AVHRR) on the NOAA series of weather satellites (NOAA 6-12) are contaminated by instrumentation noise. The signal to noise ratio (S/N) varies considerably from image to image and the between sensor variation in S/N can be large. The characteristics of the channel noise in the image data are examined using Fourier techniques. A Wiener filtering technique is developed to reduce the noise in the channel 3 image data. The noise and signal power spectra for the Wiener filter are estimated from the channel 3 and channel 4 AVHRR data in a manner which makes the filter adaptive to observed variations in the noise power spectra. Thus, the degree of filtering is dependent upon the level of noise in the original data and the filter is adaptive to variations in noise characteristics. Use of the filtered data to improve image segmentation, labeling in cloud screening algorithms for AVHRR data, and multichannel sea surface temperature (MCSST) estimates is demonstrated. Examples also show that the method can be used with success in land applications. The Wiener filtering model is compared with alternate filtering methods and is shown to be superior in all applications tested. >

A fast filtering algorithm for image enhancement

Abstract: A filtering algorithm for fast image enhancement is described. The algorithm tries to make the minimum modification on the original image structures while it performs noise smoothing at a given filtering level. The filtered image is a weighted combination of four subimages obtained from low-pass filtering the original image along four major directions. The weighting on each subimage is controlled by the differences between these subimages and the original image The resulting image is then nonsymmetrically sharpened to enhance the image structure boundaries, The overall effect of this filtering structure is effective adaptive noise reduction and edge enhancement with an efficient implementation using array processors. The high regularity and parallelism of the algorithm also makes it suitable for its efficient implementation using very large scale integrated (VLSI) circuits or multiprocessor systems. The performance of the algorithm in effectively reducing image noise and preserving/enhancing important image structures is discussed and demonstrated using several MR images from a low-field-strength MR imaging system. >

High-speed median filter designs using shiftable content-addressable memory

Abstract: This paper presents a very efficient VLSI architecture for real-time median filtering as requested in many image/video applications. The median is obtained by first sorting input sequences and then selecting identified order according to the number of inputs. To reach the goal of high-speed data sorting, an optimized delete-and-insert algorithm is derived and then mapped onto shiftable content-addressable memory architecture. The complete design can be decomposed into a set of processor elements, where each processor element consists of two basic cells-sort-cell and compare-cell. Thus the design becomes very regular. More specifically any specified order can be obtained within one cycle and a high-speed clock rate can be achieved. A prototype chip for 64 samples based on this architecture has been implemented and tested. Results show that a clock rate up to 50 MHz can be achieved using a 1.2 /spl mu/m CMOS double metal technology. >

Low residual noise speech enhancement utilizing time-frequency filtering

Abstract: Spectral subtraction is a well known technique for enhancing speech corrupted by additive wideband noise. In this technique, the "clean" signal is approximated by subtracting a noise estimate from the spectrum of the corrupted signal. A negative side effect is the residual "musical" noise that is produced when isolated spectral peaks exceed the noise estimate. In this paper, a low residual noise enhancement method is presented. This method is based on spectral subtraction but incorporates an algorithm developed to suppress "musical" noise without affecting speech. The algorithm is referred to as time-frequency filtering because spectral peaks due to noise are eliminated on the basis of duration, bandwidth, and proximity to other peaks. Results showing the effects of combining spectral subtraction and time-frequency filtering are given. >

High sample rate array architectures for median filters

Abstract: Presents high sample rate semi-systolic array architectures for computing 1D and 2D nonrecursive and recursive median filters. A high sample rate is obtained by pipelining the computations in each processor. Although the nonrecursive filters are pipelined by placing latches in the feedforward paths, the recursive filters are restructured to create additional delays in the feedback paths, and then pipelined using the delays as latches. >

Multilevel nonlinear filters for edge detection and noise suppression

Abstract: Two new classes of multilevel nonlinear filters are introduced for simultaneous edge detection and noise suppression, which the authors call a nested median filter/median averaging filter (NMF/MAF) pair and a delayed decision filter/embedded median trimmed filter (DDF/EMTF) pair. Median filters and median-related filter cause an edge shift in the presence of an impulse near the edge. The proposed filters reduce such edge shifting while suppressing impulsive as well as nonimpulsive noise. It is shown that at the noisy edge point the NMF and the DDF are substantially superior both theoretically and experimentally to the median filter, the /spl alpha/-TM filter, and the STM filter in two respects: (1) the output bias error and (2) the output mean square error. It is also shown that in the noisy homogeneous region (nonedge point), the bias errors of the MAF are zero and the output mean square errors of the MAF are substantially close to those of the optimized single-level filters: the averager, the median filter, and the min-max filter under Gaussian, Laplacian, and uniform noise, respectively. Test results confirm that the NMF/MAF pair and the DDF/EMTF structure are each robust in preserving sharp edges, inhibiting edge shifting, and suppressing a wide variety of noise. >

Functional optimization properties of median filtering

Abstract: In this letter, we use a new approach for studying the properties of median filtering. Specifically, using threshold decomposition, it is shown that median filtering operation minimizes a two-term cost function of the output state of the median filter. The first term of the cost function measures the smoothness between the median filter output and its neighbor points within the operation window, and the second term measures the discrepancy between the filter output and its original signal. The results from this study have provided us with a new tool to analyze and understand some of the properties of the median filtering operation, including weighted median filtering. >

Image restoration by adaptive-neighborhood noise subtraction.

Abstract: A new algorithm for image restoration in the presence of additive white Gaussian noise is presented. This algorithm is based on a new, adaptive method to estimate the additive noise. The basic idea in this technique is to identify uniform structures or objects in the image by use of an adaptive neighborhood and to estimate the noise and the signal content in these areas separately. The noise is then subtracted selectively from the seed pixel of the adaptive neighborhood, and the process is repeated at every pixel in the image. The algorithm is compared with the adaptive two-dimensional least-mean-squares and the adaptive rectangular-window least-mean-squares algorithms for noise suppression. The results from the application of these algorithms to synthesized images and natural scenes are presented along with mean-squared-error measures. The new algorithm performs better than the other two methods both in terms of visual presentation and mean-squared error.

Adaptive neighborhood mean and median image filtering

Abstract: A new approach to adaptive-neighborhood or region-based noise filtering is presented. The basic idea in this technique is to identify contextually related features in the image and to carry out statistical filtering operations using the pixels in these areas. Neighborhoods in the image are identified as sets of pixels that are 8-connected to the reference or seed pixel and are within a specified gray-level tolerance of the seed pixel. Thus, operations are based on contextual details in the image rather than an arbitrary grouping ofpixels (as in 3 x 3 filtering). These methods are applied to synthesized and natural images, and it is shown both quantitatively and qualitatively that adaptive-neighborhood filtering techniques are superior to analogous fixed-neighborhood filtering techniques.

Edge detection in ultrasound speckle noise

Abstract: Presents a statistical approach to edge detection in ultrasound speckle, and uses actual noise statistics to derive an expression for an optimal detection rule. The authors compute the optimal detector for the special case of uncorrelated speckle, and an approximation to the optimal detector for the case when signal-to-noise ratio (SNR) is high. They also discuss a simple sub-optimal detection scheme, and show that its performance is close to that of the optimal uncorrelated speckle detection rule, and surpasses that of the high-SNR approximation except in the case of extremely high SNR, in which case they both perform equally well. >

Suppression of coherent and incoherent noise using a microphone array

Abstract: In this paper we present a multimicrophone array for adaptive speech processing in a multipath and reverberant environment. Our proposed system for noise reduction consists of two stages: first the incoherent noise components are removed by Wiener filtering of the received signals. In a second step the possible coherent noise sources, which cannot be suppressed by the Wiener filtering are removed by an additional multichannel noise canceller. A theoretical description of the proposed system is given which shows that this method is able to reduce incoherent as well as coherent noise components and is therefore applicable in realistic conditions.

Adaptive post-processing algorithms for low bit rate video signals

Abstract: A new adaptive post-processing algorithm to enhance the quality of a noisy video sequence is presented. This algorithm employs motion compensated frame averaging to improve picture quality. A classification algorithm divides sub-blocks of pixels in the averaged frame into 4 classes: edge, smooth, non-smooth with motion and non-smooth without motion. Spatial algorithms that perform multilevel median filtering, double median filtering and median filtering are used for pixels belonging to edge, smooth and non-smooth with motion categories. Pixels in the nonsmooth, unmoving category are left unfiltered to preserve corresponding image texture. In a simpler version of this 4-class system, the motion cues and motion compensated frame averaging are eliminated, and the purely spatial filtering is based on a 3-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. >

Scan converter of television receiver and scan converting method thereof

Abstract: A method and apparatus is provided for increasing scanning line numbers for TV video signal utilizing a three dimensional medial filter. The method and apparatus inhibit the picture quality from being deteriorated due to a vertical resolution decreasing, shimmering and step edge phenomenon and the like occurring at the intra-field interpolation system, inter-field interpolation system and motion adaptive interpolation system which are conventional scanning line number increasing systems that increase TV scanning line number. The method and apparatus includes a three dimensional median filter which applies a weighing factor to pixel components of horizontal, vertical and diagonal directions of pixel component's periphery to be interpolated by the horizontal, vertical and time axis judging signals, and which can be applied for increasing the scanning line numbers of the luminance signal or the chrominance signal in an improved definition television IDTV receiver or an extended definition television EDTV receiver.

Subband coding and noise reduction in multiresolution analysis frames

Abstract: We consider the effect of the quantization noise introduced by coding at subbands. We demonstrate that significant noise reduction is achieved by using wavelet frames and their associated filter banks in a subband signal processing system.

Efficient detail-preserving method of impulse noise removal from highly corrupted images

Abstract: Images are often corrupted by impulse noise due to faulty image acquisition devices or during transmission. The goal of impulse noise removal is to suppress the noise while preserving edges and details. In this paper, an efficient method is proposed to remove impulse noise from a highly corrupted image while preserving the detail features. This new method is based on a detection-estimation strategy, in which the impulse noise is detected first using a quadratic filter, and a selectively chosen local mean is used to estimate the true value of the corrupted pixel. Simulation results indicate that the new method is much more efficient and effective than the median filtering and the out-range methods.

Analogue median circuit

Abstract: A new analogue median circuit with a very sharp DC transfer characteristic is described. The configuration is based on a multiple-input current comparator and can be generalised to other rank extractors. A prototype has been fabricated in a 2 µm CMOS technology, and experimental results are presented.

Relative effectiveness of neural networks for image noise suppression

Abstract: This paper describes experiments which show not only that an ANN structure can be highly effective in removing noise from digital images but also that it can be adapted, by training, to several different types of noise and can remove each of them as effectively as more conventional types of filter. We conclude that the property of adaptivity gives the ANN structure a definite advantage over (for example) the median filter, which has no adjustable parameters other than neighbourhood size.