Showing papers on "Median filter published in 1974"
TL;DR: Digital computer simulations of grain noise suppression using two particular cases of this additive, "signal-modulated" noise model were performed, demonstrating the potential advantages of noise suppression filters which make use of a priori knowledge of the signal-dependent nature of the grain noise.
Abstract: Image detection noise is a fundamental limitation in picture processing, whether analog or digital. This noise is characteristically signal-dependent and this signal-dependence introduces significant problems in the design of appropriate noise-suppression techniques. This paper outlines some recent results obtained by the authors in the optimum suppression of two types of signal-dependent image noise: film-grain noise and photoelectron shot noise. The work in grain noise suppression involves deriving the minimum-mean-square error Wiener filter for a new form of signal-dependent noise model suggested in earlier work by T. S. Huang. Implementation of these filters by either coherent optical or digital processing techniques is possible. Digital computer simulations of grain noise suppression using two particular cases of this additive, "signal-modulated" noise model were performed. They demonstrate the potential advantages of noise suppression filters which make use of a priori knowledge of the signal-dependent nature of the grain noise. The results of work on linear, unbiased restoration of images recorded in the presence of photoelectron noise are summarized. Additional work in both of these areas is suggested, with a particular need existing for correlating the properties of various models proposed for grain noise with experimental data obtained on emulsions using scanning microdensitome ters.
78 citations
TL;DR: Simulations using noise recordings from a number of widely separated locations in the world have shown improvements of 7 dB to 20 dB at times of high ELF atmospheric noise levels at the receiver input.
Abstract: This paper describes the design of a candidate noise processor for the Sanguine receiver based on communication theory considerations and detailed experiments using wide-band recordings of extremely low-frequency (ELF) (3-300 Hz) atmospheric noise. This processor consists of the following elements: 1) a compensating (or whitening) filter; 2) nonlinear notch filtering at frequencies of manmade interference; 3) a post-notch filter nonlinearity; and 4) a phase coherent linear matched filter. Due to the impulsive non-Gaussian nature of the noise, nonlinear processing with a bandwidth considerably greater than the 40-80-Hz signal bandwidth is significantly better than a linear receiver (consisting only of a matched filter and appropriate whitening filters). Simulations using noise recordings from a number of widely separated locations in the world have shown improvements of 7 dB to 20 dB at times of high ELF atmospheric noise levels at the receiver input.
51 citations
TL;DR: A simple method of calculating the steady-state value of the variance of the output noise of a digital filter due to the input quantization noise or internally generated noise from product round-off is presented.
Abstract: A simple method of calculating the steady-state value of the variance of the output noise of a digital filter due to the input quantization noise or internally generated noise from product round-off is presented. The output noise is expressed as a sum of simpler terms belonging to one of four basic groups. Explicit expressions have been developed for rapid evaluation of these terms in the expansion. The method is illustrated by means of examples.
42 citations
TL;DR: A previously developed algorithm designed for use in the case of stationary noise is modified to allow estimation of an unknown Kalman gain and thus the system state in the presence of unknown time varying noise statistics.
Abstract: The problem of estimating the state of a linear dynamic system driven by additive Gaussian noise with unknown time varying statistics is considered. Estimates of the state of the system are obtained which are based on all past observations of the system. These observations are linear functions of the state contaminated by additive white Gaussian noise. A previously developed algorithm designed for use in the case of stationary noise is modified to allow estimation of an unknown Kalman gain and thus the system state in the presence of unknown time varying noise statistics. The algorithm is inherently parallel in nature and if implemented in a computer with parallel processing capability should only be slightly slower than the stationary Kalman filtering algorithm with known noise statistics.
42 citations
TL;DR: In this article, it is shown that a minimization of the filter attenuation sensitivity, which is characteristic of wave digital filters, serves to reduce the roundoff noise generated by arithmetic operations in a digital-filter computational sequence.
Abstract: Roundoff noise generated by arithmetic operations in a digital-filter computational sequence is undesirable in that it serves to distort the true signal at the output Furthermore, coefficient wordlength is directly related to the generated noise It is shown that a minimization of the filter attenuation sensitivity, which is characteristic of wave digital filters, serves to reduce the noise Analytical results confirm this for both floating-point and fixed-point systems A simulation where the actual noise is measured produces results which demonstrate the superior performance of the wave digital filter over the standard z-transform filter
12 citations
TL;DR: A new system is proposed that overcomes the problems of the previous noise suppression techniques and experimental results of the new system are presented along with a determination of the resolution of the system.
Abstract: In optical data processing the quality of the output image is usually degraded by diffraction noise generated by the optical components of the system. The suitability of previously suggested techniques to a dc spatial filtering processor is discussed. A new system is proposed that overcomes the problems of the previous noise suppression techniques. Experimental results of the new system are presented along with a determination of the resolution of the system. The errors and limits of the new coherent noise suppression system are discussed.
6 citations
TL;DR: A technique is presented for the calculation of the optimum-lag shaping filter for a contaminated signal wavelet and it is shown that this method automatically gives the filter with the optimum combination of shaping performance and noise reduction.
Abstract: Approximate deconvolution by means of Wiener filters has become standard practice in seismic data-processing. It is well-known that addition of a certain percentage of noise energy to the autocorrelation of the signal wavelet leads to a filter that does not increase, or even reduces, the noise level on the seismogram. This noise addition will, in general, cause a minimum phase signal to become mixed phase. A technique is presented for the calculation of the optimum-lag shaping filter for a contaminated signal wavelet. The advantages of this method over the more conventional approach are that it needs less arithmetic operations and that it automatically gives the filter with the optimum combination of shaping performance and noise reduction.
3 citations
TL;DR: In this paper, the reliability of human detection of certain classes of signals of simple shape embedded in low-pass filtered white gaussian noise recorded on paper was compared with performance of an optimum matched filter detector which operated on the same set of data.
Abstract: This report is concerned with the reliability of human detection of certain classes of signals of simple shape embedded in low-pass filtered white gaussian noise recorded on paper. Results are compared with performance of an optimum matched filter detector which operated on the same set of data. The signal-to-noise ratio obtained at the output of such a filter can be used to predict the reliability of detection and the false alarm rate in subjects' performance. For signal-to-noise ratio greater than one, subjects make fewer errors (i.e., misses plus false alarms) than the optimum filter, and the difference is a monotonically decreasing function of the signal-to-noise ratio. Factors concerned with these two observations are discussed.
3 citations
16 Jul 1974
TL;DR: A new threshold scheme that adaptively reduces noise effect in a data processing system that receives signals corrupted by non-stationary colored noise is presented, representing a large reduction in computation time and hardware circuit elements.
Abstract: Wide band digital signal processing systems are becoming increasily important due to the advance of high speed digital Fourier analyzers. This paper presents a new threshold scheme that adaptively reduces noise effect in a data processing system that receives signals corrupted by non-stationary colored noise. The corrupted signal is decomposed into spectral segments by a discrete Fourier transform (DFT) unit and the resulting spectrum is processed by an adaptive threshold unit. The adaptive threshold network is tasked to estimate the noise level and to subtract this noise estimate from the composite signal plus noise to arrive at a high quality signal. For each set of DFT outputs, a finite order polynomial is used to approximate the average noise level by least squares method. The key feature in this polynomial approach is the reduction of the number of parameters characterizing the threshold. The results of this work show that second order can be sufficient, hence comparing to actual number of spectral values (generally greater than 512), the polynomial approach represents a large reduction in computation time and hardware circuit elements. The adaptive algorithm used in this work to estimate the -parameters sequentially is derived from adaptive estimation theory, which has been successfully implemented in a digital signal processing system.
2 citations