Showing papers on "Median filter published in 1979"

A fast two-dimensional median filtering algorithm

Abstract: We present a fast algorithm for two-dimensional median filtering. It is based on storing and updating the gray level histogram of the picture elements in the window. The algorithm is much faster than conventional sorting methods. For a window size of m × n, the computer time required is 0(n).

Adaptive structures for multiple-input noise cancelling applications

Abstract: This paper is concerned with problems in which the interference present in a primary signal is reduced using a sum of M linearly-filtered reference signals. These latter signals contain interference components which are correlated with that present in the primary. Examples occur in antenna array processing and in multiple-axis seismometer recordings of geophysical data. In the structures of interest, the linear filters are adaptive and employ a lattice configuration. Previous work in this area has been restricted to the case of a single reference signal. The multiple-reference case is shown to have unique characteristics which do not appear in the single-dimensional case. Examples illustrating this difference are presented.

Fast on-line implementation of two-dimensional median filtering

Abstract: A fast two-dimensional median filtering technique is proposed for on-line image-processing applications, using a minicomputer. The histogram of the picture elements enclosed in the filter window (L × L) is computed and the median element is sequentially evaluated by successive corrections of the previous output. A comparison with an efficient sorting method is carried out in terms of computing time and memory requirements.

An Edge-Preserving Recursive Noise-Smoothing Algorithm for Image Data

Abstract: Recursive Kalman filters are often used for noise reduction in image data. These linear filters are based on the second-order statistics of image and noise. The noise is effectively reduced by the filtering operation, but the edges in the image are blurred and image contrast is reduced as well. These effects decrease the subjective quality of the image. A simple and computationally fast scan-ordered one-dimensional Kalman filter is derived, which is then provided with additional structural information about the edges in the noisy image. This filter behaves like the original noise-smoothing Kalman filter if no edges are present but has a greatly improved step response. In this way the edge-blurring phenomenon is effectively reduced. Results of several experiments are presented to demonstrate the feasibility of our approach.

Adaptive digital techniques for audio noise cancellation

Abstract: Discusses two adaptive digital techniques for audio noise cancellation. The first technique, adaptive predictive deconvolution, uses an adaptive linear predictor to estimate and cancel (time) correlated noise components on an audio signal. The second technique, adaptive filtering, employs two audio signal inputs, the first having the desired audio signal along with noise and the second sensing principally the noise. The second, noise signal is adaptively filtered and subtracted from the first signal cancelling noise components common to the two inputs. A stand-alone digital signal processor has been developed to carry out these two noise cancellation techniques. This automatic digital audio processor, ADAP, carries out these processes in real time with up to a 256th order digital filter.

Digital Filtering of Speckle-photography Data

Abstract: Optical methods for nondestructive analysis of strain fields are continuing to generate a great deal of interest among experimentalists; the potential of these methods is considered to be great. One persistent obstacle to the accuracy of such methods is the contamination of the data by ‘optical noise’. This paper describes a digital method which allows extensive reduction of noise components from data records obtained in point-by-point interrogation of double-exposure speckle photographs. The computations prove to be inexpensive and effective. Sample results are given and further applications are suggested.

Noise cancellation via linear prediction filtering

Abstract: In many applications of Wiener filtering to noise cancellation an external reference noise input is required. However, if an external reference is not available, it is still possible to suppress additive noise using a Wiener linear prediction filter (LPF) provided the signal bandwidth is significantly less than the noise bandwidth. The purpose of this paper is to provide an analytical basis for bounding the performance of a digital LPF when applied to the problem of cancelling broadband additive noise from narrow-band signals.

Fast Median Filter Implementation

Abstract: The Tukey median filter is widely used in image processing for applications ranging from noise reduction to dropped line replacement. However, implementation of the median filter on a general-purpose computer tends to be computationally very time-consuming. This paper describes a new median filter implementation suitable for use on the video-rate "pipeline processors" provided by several commercially-available image display systems. The execution speed of the new implementation is faster than the best software implementations, depending on the median filter window size, by up to an order of magnitude. It is also independent of the image dimensions up to a 512x512 pixel size.

Median Masking Technique For The Enhancement Of Digital Images

Abstract: A nonlinear masking technique has been developed which characterizes digital images by local measures of the median and the median absolute deviation (MAD). Space-variant enhancement is elicited by modifying the local MAD as calculated over a moving window in the original image. The method is found to be effective in edge enhancement and noise cleaning operations.© (1979) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

A Class of Distribution-Free Detectors for Pulse Amplitude Modulated Signals

Abstract: A class of distribution-free detectors for M -ary pulse amplitude modulated digital signals in the presence of certain signal dependent interference and additive noise of unknown distribution is formulated, evaluated in terms of probability of error and compared to a matched filter detector in Gaussian noise. The noise distribution is unknown except that it satisfies certain broad assumptions and that its value at the median is known. A decision method based on the value of a test statistic is developed. An analytical expression for the average probability of error and a method of calculating the optimum decision thresholds that minimize this probability of error are presented. The probability of error is independent of the form of the noise distribution but depends on the value of the noise density function at the median. An example of a ternary signal set in white Gaussian noise is worked out for illustration. The optimum decision thresholds are calculated and the resulting average probability of error is plotted as a function of signal-to-noise ratio. Average probability of error of a matched filter detector is also evaluated under similar conditions and a comparison is made with that of the proposed detector.

Comparative Computer Simulation of Several Signal to Noise Enhancement Techniques

Abstract: Techniques for signal to noise ratio enhancement (filtering) of noisy experimental data are compared by comprehensive computer simulation using synthetic data. The four important descriptors: signal to noise ratio enhancement, peak width increase, peak maximum displacement, and computation time on a laboratory minicomputer are determined for each filter type studied. Comparative results indicate important trends and considerations of signal processing as well as providing a foundation for the selection of filter designs for a given instrumental situation.

Heuristic Approach For Video Image Enhancement

Abstract: Video Image Enhancement through adaptive noise filtering and edge sharpening is presented. The basic concepts behind this technique are the fact that with some kind of image segmentation, noise filtering can be performed in the nearly uniform region and edge sharpening only near an edge. The resulting algorithm is nonlinear and adaptive. It adapts globally to the input SNR and locally to the gradient magnitude. Implementation is quite simple. Performance is nonlinear and depends on the SNR of the original image. Effective video signal-to-noise ratio can be improved with minimal observable contouring effect, degradation in spatial resolution, and other artifacts.

Image detection in the presence of speckle

Abstract: Detection of binary images is considered in the presence of both speckle and additive noise. The detector performance has been evaluated in the presence of speckle as well as in the absence of it. It is observed that the presence of speckle increases the probability of error appreciably even at high signal-to-noise ratios (SNR's).

A Comparison of Image Enhancement Techniques.

Abstract: : After a discussion of the statistical theory of image fields, the paper provides a critical comparison of the median filtering, autoregressive (AR), autoregressive and moving average (ARMA), and Kalman filtering of reconnaissance imagery with additive Gaussian noise. The Kalman filtering performs the best in image enhancement and requires relatively less amount of computation time. (Author)

Reduction of Quantization Noise in PCM Image Coding.

Abstract: : A new technique to reduce the effect of quantization in PCM image coding is presented in this report. The new technique consists of Roberts' pseudonoise technique followed by a noise reduction system. The technique by Roberts effectively transforms the signal dependent quantization noise to a signal independent additive random noise. The noise reduction system that follows reduces the additive random noise. Some examples are given to illustrate the performance of the new quantization noise reduction system. (Author)

Reduction Of Quantization Noise In Pulse Code Modulation (PCM) Image Coding

Abstract: A new technique to reduce the effect of quantization in PCM image coding is presented in this paper. The new technique consists of Roberts' pseudonoise technique followed by a noise reduction system. The technique by Roberts effectively transforms the signal dependent quantization noise to a signal independent additive random noise. The noise reduction system that follows reduces the additive random noise. Some examples are given to illustrate the performance of the new quantization noise reduction system.

Hybrid method of 2D-image restoration and filtering using an interactive analog system of video-processing

Abstract: We used the optical method of image processing based on the defocusing properties of a transfer lens and the analog processing of video signals produced by both the scanned image and the image stored on a disc memory, to filter the photographic noise and to restore the picture. The method is comparable to Wiener filtering with the assumption of additive noise and an isotropic shift-invariant smearing function. Several examples show the usefulness of the method for different cases like a picture of the globular cluster M15 or a picture of the solar granulation. The method is also compared to the digital one.

Image Restoration With A Locally Variable Wiener Filter

Abstract: The parametric Wiener filter is often used to deblur images that are relatively noise-free. If noise is more severe, the restored image may be obscured by a granular pattern that results when the noise is subjected to the deblurring filter. This effect may be reduced by using a larger noise parameter, but this leads to a restoration that is less sharp. We describe how the noise parameter may be varied from pixel to pixel, so that it is larger only where noise is greater. Pixels with low signal-to-noise ratios are identified by a thresholding process and by comparison with nearest neighbors. The effects of the estimated Wiener spectra on the restored image are discussed.

Simplified error models for digital filters

Abstract: Statistical error models for digital filter roundoff noise estimation are usally based on a set of noise sources interconnected according to the filter structure. The location of the noise sources depends on the location of the quantizers within the filter network. Roundoff noise analysis by both, simulation of digital filters and analysis of noise models yields the result that the entire roundoff noise behaviour even of multimesh digital filters can be described by exactly one quatizer if the noise power is not too great. In this contribution the assumptions for the conventional statistical noise analysis are discussed in detail Also practical methods for the determination of the characteristics of the one-quantizer-model are given. The applicability of the proposed simplified error model is demonstrated by the roundoff noise analysis of different filter structures utilizing various quantizers.

On state-space signal processing with application to image enhancement

Abstract: INTEODUCTION Recently there has been much interest in the use in digital signal processing [11. Since the state variables ae used, this class of techniques will be called state—space signal processing in this paper. In particular the paper is concerned with the application of Kalman filtering and related recursive estimation techniques for image enhancement. There has been nuch work on this Without the object boundaries, an image nay be modelled as a honogeneous random field. Wiener filtering, Kaiman filtering, and nany other procedures nay be used to snooth the image. Kalman filtering has the advantages over the others in that it is suitable for real-tine operation, requiring little parametric information of the image model, and that it can adapt to the textural and temporal variations in the image. In practice the cbject boundaries should be considered in image enhancement and the image should be mcdelled as a shift—variant system rather than a shift—invariant system. Kalman filtering is particularly suitable for such an image model. Recently an adaptive filtering method has been proposed for detection and estimation of the jumps by using the Kalman filter and a generalized likelihood ratio technique [6]. The basic idea is that Kalman filter is implemented on the assumption that there is no state jumps, and a second system is designed to monitor the measurement residuals of the filter to determine if a change has occurred and adjust the filter accordingly. When the transition matrix of the Kalmam filter is unknown, it can be determined by a method of simultaneous estimation of parameters and states [7]. The combination of the work in Ref s. 6 & 7 is adpated to image enhancement with reconnaissance imagery. The resulting improvement even under very small signal— to—noise ratio is very significant. Detailed mathematical formulation and computer results are presented in the following sections. It should be emphasized that the mathematical techniques presented are very useful to all state—space signal processing problems.