Showing papers on "Median filter published in 1997"

SUSAN—A New Approach to Low Level Image Processing

Abstract: This paper describes a new approach to low level image processing; in particular, edge and corner detection and structure preserving noise reduction. Non-linear filtering is used to define which parts of the image are closely related to each individual pixel; each pixel has associated with it a local image region which is of similar brightness to that pixel. The new feature detectors are based on the minimization of this local image region, and the noise reduction method uses this region as the smoothing neighbourhood. The resulting methods are accurate, noise resistant and fast. Details of the new feature detectors and of the new noise reduction method are described, along with test results.

Generalized multichannel image-filtering structures

Abstract: Recent works in multispectral image processing advocate the employment of vector approaches for this class of signals. Vector processing operators that involve the minimization of a suitable error criterion have been proposed and shown appropriate for this task. In this framework, two main classes of vector processing filters have been reported in the literature. Astola et al. (1990) introduce the well-known class of vector median filters (VMF), which are derived as maximum likelihood (ML) estimates from exponential distributions. Trahanias et al. (see ibid., vol.2, no.4, p.528-34, 1993 and vol.5, no.6, p.868-80, 1996) study the processing of color image data using directional information, considering the class of vector directional filters (VDF). We introduce a new filter structure, the directional-distance filters (DDF), which combine both VDF and VMF in a novel way. We show that DDF are robust signal estimators under various noise distributions, they have the property of chromaticity preservation and, finally, compare favorably to other multichannel image processing filters.

Noise estimation and filtering using block-based singular value decomposition

Abstract: Preprocessing of image and video sequences with spatial filtering techniques usually improves the image quality and compressibility. We present a block-based, nonlinear filtering algorithm based on singular value decomposition and compression-based filtering. Experiments show that the proposed filter preserves edge details and can significantly improve the compression performance.

Robust motion-compensated video upconversion

Abstract: The quality of field-rate conversion improves significantly with motion-compensation techniques. It becomes possible to interpolate new fields at their correct temporal position. This results in smooth motion portrayal without loss of temporal resolution. However, motion vectors are not always valid for every pixel or object in an image. Therefore, visible artifacts occur wherever such wrong vectors are used on the image. One effective method to solve this problem is the use of non-linear filtering. In this method, a wrongly interpolated pixel is either substituted or averaged with neighbouring pixels. We introduce and evaluate a new and very robust upconversion algorithm which is based on the non-linear filtering approach. It is unique in that it estimates motion vector reliability and uses this information to control the filtering process. This algorithm outperforms others in its class, especially when we have complex image sequences.

A property of the minimum vectors of a regularizing functional defined by means of the absolute norm

Abstract: We consider a regularizing functional defined by means of the l/sub 1/ norm, where the regularization is obtained using first differences; as is well-known, such a functional can be put in relation with recursive median filters of appropriate window length. We show that at least one of the minima is reached at a vector, whose components have values over the same discrete set of the given signal. This suggests a simple method to refine the approximate solution to the regularization problem, which can be obtained with recursive median filters of increasing order. We also report an example of application, where the refinement method is employed for a signal detection problem.

Mapping defects or dirt dynamically affecting an image acquisition device

Abstract: Defects such as dirt, dust, scratches, blemishes, pits, or defective elements or pixels in a CCD, scanner, photocopier, or image acquiring device are dynamically detected by processing a plurality of images via a computer. A pristine object of calibration is not required. Stationary components of the video images are found and detected so as to produce a low false alarm probability. Text segmentation and measurement of total deviation based on variability related to high-frequency components of the video image are employed to prevent applying the process or method to printed text or graphics. Additional techniques optionally employed are median filtering, sample area detection, and dynamic adjustment of scores. In special cases, only moderately blank documents are used. The dynamic defect detection allows defect compensation, defect correction, and alerting the operator of defects.

Blotch and Scratch Detection in Image Sequences based on Rank Ordered Differences

Abstract: Publisher Summary Old movies are often valuable historical records, but most of them progressively deteriorate in visual quality during the years, decreasing their usefulness. To avoid distortions in the unaffected parts of the image, first the locations of the blotches and scratches have to be detected before the restoration algorithm can be applied. This chapter proposes an algorithm based on rank ordered differences (ROD) that are calculated from the data of the current image flame, and the preceding and succeeding motion-compensated frame. The ROD detector presented in the chapter is a modified form of the signal-dependent rank ordered mean filter (SD-ROM) used for restoration of an impulse noise corrupted image. While the SD-ROM filter works exclusively in the spatial area of one image frame and is only able to remove one or two pixel wide distortions, the ROD filter is designed to work on image sequences. It is able to detect both thin scratches and blotches. The chapter also compares the new algorithm to existing detection algorithms in the form of probability plots and images indicating the correct, false, and missing detections.

Using local median as the location of the prior distribution in iterative emission tomography image reconstruction

Abstract: Iterative reconstruction algorithms like MLEM (Maximum Likelihood Expectation Maximization) can be regularized using a weighted roughness penalty term according to certain a priori assumptions of the desired image. In the R?RP (Median Root Prior) algorithm the penalty is set according to the deviance of a pixel from the local median. This allows both noise reduction and edge preservation. The prior distribution is Gaussian located around the median of a neighborhood of the pixel. Non-monotonic details smaller than a given limit are considered as noise and are penalized. Thus, MRP implicitly contains the general description of the characteristics of the desired emission image, and good localization of tissue boundaries is achieved without anatomical data. In contrast to the MLEM method, the number of iterations needs not be restricted and unlike many other Bayesian methods MRP has only one parameter. The penalty term can be applied to various iterative reconstruction algorithms. The assumption that the true pixel value is close to the local median applies to any emission images, including the 3D acquisition and images reconstructed from parametric sinograms.

Wavelet-domain filtering for photon imaging systems

Abstract: Many imaging systems rely on photon detection as the basis of image formation One of the major sources of error in these systems is Poisson noise due to the quantum nature of the photon detection process Unlike additive Gaussian noise, Poisson noise is signal-dependent, and consequently separating signal from noise is a very difficult task In this paper, we develop a novel wavelet-domain filtering procedure for noise removal in photon imaging systems The filter adapts to both the signal and the noise and balances the trade-off between noise removal and excessive smoothing of image details Designed using the statistical method of cross-validation, the filter is simultaneously optimal in a small-sample predictive sum of squares sense and asymptotically optimal in the mean square error sense The filtering procedure has a simple interpretation as a joint edge detection/estimation process Moreover, we derive an efficient algorithm for performing the filtering that has the same order of complexity as the fast wavelet transform itself The performance of the new filter is assessed with simulated data experiments and tested with actual nuclear medicine imagery

Apparatus and method for signal dependent noise estimation and reduction in digital images

Abstract: The present invention efficiently and quickly estimates signal (code value) dependant noise in an image and subsequently reduces that noise. The digital image is segmented both according to code value and into smooth vs. textured regions. Noise estimates taken from smooth regions are used to model noise as a function of code value. The predictions of the model are used to tone noise reduction in all areas of the image according to the appropriate code value.

A new technique for noise filtering of SAR interferogram phase images

Abstract: This paper addresses the noise filtering problem for SAR interferogram phase images. The phase noise is characterized by an additive noise model, and a filtering algorithm based on this noise model was developed by filtering noise along fringes. In addition, this filter adaptively adjusts the amount of filtering according to the coherence. The effectiveness of this filter is demonstrated using SIR-C/X-SAR multi-pass generated interferograms.

A noise-filtering method using a local information measure

Abstract: A nonlinear-noise filtering method for image processing, based on the entropy concept is developed and compared to the well-known median filter and to the center weighted median filter (CWM). The performance of the proposed method is evaluated through subjective and objective criteria. It is shown that this method performs better than the classical median for different types of noise and can perform better than the CWM filter in some cases.

Improved median filter using minmax algorithm for image processing

Abstract: A new algorithm which incorporates standard median filtering is proposed for effectively removing impulsive noise in image processing. This computationally efficient approach first classifies input pixels and then performs a median filtering process. Simulation results show that the new scheme, regardless of high or low SNR, displays a superior mean square error (MSE) over standard median filters.

FPGA implementation of a median filter

Abstract: The median filter is an effective device for the removal of impulse-based noise on video signals. This is due to the partial averaging effect of the median filter and its biasing of the input stream, rather than straight mathematical averaging. In this paper, we describe three realizations of median filter, built into as few as one field programmable logic device, which is capable of processing an incoming video data stream at a maximum (programmable logic device partially dependent) of around 30 MS/s. In total, four designs are considered, with a primary design, two variations on the primary design and an asynchronous version based on the primary design. Simulation of the primary design (both synchronous and asynchronous) has demonstrated its potential for reducing the area requirements of a median filter whilst not sacrificing either speed or accuracy.

Model-based image enhancement of far infrared images

Abstract: We devise enhancement algorithms for far infrared images based upon a model of an idealized far infrared image being piecewise-constant. We then apply two known enhancement algorithms: median filtering and spatial homomorphic filtering, and then extend the model to develop spatio-temporal homomorphic filtering. The algorithms have been applied to several image sequences and work well, showing significant image enhancement.

Obtaining smoother singular arc policies using a modified iterative dynamic programming algorithm

Abstract: Dynamic programming is a very powerful technique for the optimization of dynamic systems. With the ready availability of high-speed computers and the development of the iterative dynamic programming (IDP) algorithm, a feasible alternative to the calculus of variations approach to the optimal control problem is now available. Inherent in the IDP algorithm is the application of piecewise constant discretized controls. This often leads to singular optimal control policies that are highly active. The aim of this research is to modify the IDP algorithm to reduce the control energy variations that are typical of IDP while at the same time developing policies that are very close to the true optimal control. This is achieved by including a filter within the IDP procedure. Two types of filtering schemes are considered: a median filter and a first-order filter. Application of this modified algorithm to two bioreactor systems that yield singular optimal control profiles is presented and the usefulness of this scheme...

Nonlinear Filtering of Non-Gaussian Noise

Abstract: This paper introduces a new nonlinear filter for a discrete time, linear system which is observed in additive non-Gaussian measurement noise. The new filter is recursive, computationally efficient and has significantly improved performance over other linear and nonlinear schemes. The problem of narrowband interference suppression in additive noise is considered as an important example of non-Gaussian noise filtering. It is shown that the new filter outperforms currently used approaches and at the same time offers simplicity in the design.

Moment-based image normalization with high noise-tolerance

Abstract: In this paper the effects of noise with nonzero mean on existing moment-based image normalization methods are studied. Several modifications to reduce noise sensitivity are presented and tested. They involve nonlinear mapping and fractional- and negative-order moments.

A new approach to vector median filtering based on space filling curves

Abstract: The availability of a wide set of multidimensional information sources in different application fields (e.g., color cameras, multispectral remote sensing imagery devices, etc.) is the basis for the interest of image processing research on extensions of scalar nonlinear filtering approaches to multidimensional data filtering. A new approach to multidimensional median filtering is presented. The method is structured into two steps. Absolute sorting of the vectorial space based on Peano space filling curves is proposed as a preliminary step in order to map vectorial data onto an appropriate one-dimensional (1-D) space. Then, a scalar median filtering operation is applied. The main advantage of the proposed approach is the computational efficiency of the absolute sorting step, which makes the method globally faster than existing median filtering techniques. This is particularly important when dealing with a large amount of data (e.g., image sequences). Presented results also show that the filtering performances of the proposed approach are comparable with those of vector median filters presented in the literature.

Identification of the nature of noise and estimation of its statistical parameters by analysis of local histograms

Abstract: This paper deals with the problem of identifying the nature of noise and estimating its standard deviation from the observed image in order to be able to apply the most appropriate processing or analysis algorithm afterwards. In this study, we focus our attention on three classes of degraded noise images, the first one being degraded by an additive noise, the second one by a multiplicative noise and the latter by an impulsive noise. First, in order to identify the nature of the noise, we propose a new approach consisting of characterizing each class by a parameter obtained from histograms computed on several homogeneous regions of the observed image. The homogeneous regions are obtained by segmenting images. Then, the estimation of the standard deviation is achieved from the analysis of an histogram of local standard deviations computed on each of the homogeneous regions.

Multistage adaptive filters for in-phase processing of line-frequency signals

Abstract: The authors describe two adaptive multistage digital filters for 50/60-Hz line-frequency signal processing in zero-crossing detectors and synchronous power systems. These filters combine a median filter with adaptive predictors, either finite-impulse response (FIR)- or infinite-impulse response (IIR)-based, thus making it possible to extract the sinusoidal signals from noise and strong disturbances without phase shifting the primary frequency signal. The median filter is used as a prefilter because it can remove deep commutation notches from the waveform. Adaptation allows the filters to track the exact instantaneous line frequency and avoids the selectivity problem encountered with a fixed filter.

Noise suppression digital filter for functional magnetic resonance imaging based on image reference data.

Abstract: The central decision in every functional magnetic resonance imaging (fMRI) experiment is whether pixels in brain tissues are showing activation in response to neural stimulus or as a result of noise. Images are degraded not only by random (e.g., thermal) noise, but also by structured noise due to MR system characteristics, cardiac and respiratory pulsations, and patient motion. A novel digital filter has been developed to suppress cardiac and respiratory structured noise in fMRI images, using estimates of structured and random noise power spectra obtained directly from the images. It is an adaptive filter based on stationary noise statistics, and is equivalent in form to a Wiener filter. A mathematical model of the filtering process was developed to understand how the strength and distribution of structured and random noise power influenced filter performance. The filter was tested using images from an auditory activation study in ten subjects. In subjects whose structured noise power was localized to a relatively narrow frequency range, a strong relationship was found, both experimentally (R = 0.975, P < 0.0004 for H 0 : R = 0) and using the model, between filter performance and the level of structured noise power contaminating the experiment frequency. The filter significantly reduced the rate of false-positive activations in the subset of subjects whose experiment frequency was relatively heavily contaminated by structured noise. Notch filters, that simply eliminate unwanted frequencies, performed poorly in all subjects. Unlike the proposed Wiener filter, these filters did not suppress structured noise power at the experiment frequency that contributes to false-positive activations.

A robust method for parameter estimation of signal-dependent noise models in digital images

Abstract: A class of signal-dependent noise models is discussed, with reference to the cases of film-grain and speckle noise, which are commonly encountered in image processing applications. The model is uniquely defined by the variance of the zero-mean random noise (independent of the signal) and by the gamma exponent which rules the dependence with the signal. A robust procedure for measuring such parameters directly from the noisy images is presented. First, the gamma coefficient is estimated from at least three homogeneous non-textured regions. Then, the noise variance is determined as the mode of the histogram of the ratio between the local variance, and the local mean raised to twice the gamma. Computer simulations show the high accuracy of the results.

Image data recursive noise filter with reduced temporal filtering of higher spatial frequencies

Abstract: An image data recursive noise filter wherein relatively high spatial frequency components of the image data are either not filtered at all or are filtered to a lesser degree than relatively low spatial frequency components of the image data. This minimizes blurring of fine low-contrast detail and also avoids "freezing" of noise in undetailed moving areas of the image.

Wavelet for speech denoising

Abstract: This paper presents the use of the wavelet transform for noise reduction in noisy speech signals. The use of different wavelets and different orders have been evaluated for their suitability as a transform for speech noise removal. The wavelets evaluated are the biorthogonal wavelets, Daubechies wavelets, coiflets as well as symlets. Also two different means of filtering the noise in the transformed coefficients are presented. The first method is based on magnitude subtraction while the second method is based on the Wiener filter with a priori signal to noise ratio estimation.

Automatic noise floor spectrum estimation in the presence of signals

Abstract: This paper describes a technique for automatically estimating the noise floor spectrum in the presence of signals. The technique works equally well for both flat and non-flat noise floor spectra. The technique is based on applying morphological binary image processing operators to a binary image of the received power spectrum. It is related to rank-order filters but is more computationally efficient. The performance is illustrated on the detection of radio signals.