Topic
Median filter
About: Median filter is a research topic. Over the lifetime, 12479 publications have been published within this topic receiving 178253 citations.
Papers published on a yearly basis
Papers
More filters
TL;DR: Under the framework of switching median filtering, a highly effective algorithm for impulse noise detection is proposed aiming at providing solid basis for subsequent filtering and in principle simpler as it is intuitive and easy to implement as it has uncomplicated structure and few codes.
Abstract: Under the framework of switching median filtering, a highly effective algorithm for impulse noise detection is proposed aiming at providing solid basis for subsequent filtering. This algorithm consists of two iterations to make the decision as accurate as possible. Two robust and reliable decision criteria are proposed for each iteration. Extensive simulation results show that the false alarm rate and miss detection rate of the proposed algorithm are both very low and substantially outperform existing state-of-the-art algorithms. At the same time, the proposed algorithm is in principle simpler as it is intuitive and it is easy to implement as it has uncomplicated structure and few codes.
98 citations
Posted Content•
TL;DR: This paper attempts to undertake the study of three types of noise such as Salt and Pepper (SPN), Random variation Impulse Noise (RVIN), Speckle (SPKN) and they are compared with one another to choose the base method for removal of noise from remote sensing image.
Abstract: This paper attempts to undertake the study of three types of noise such as Salt and Pepper (SPN), Random variation Impulse Noise (RVIN), Speckle (SPKN). Different noise densities have been removed between 10% to 60% by using five types of filters as Mean Filter (MF), Adaptive Wiener Filter (AWF), Gaussian Filter (GF), Standard Median Filter (SMF) and Adaptive Median Filter (AMF). The same is applied to the Saturn remote sensing image and they are compared with one another. The comparative study is conducted with the help of Mean Square Errors (MSE) and PeakSignal to Noise Ratio (PSNR). So as to choose the base method for removal of noise from remote sensing image.
98 citations
TL;DR: A new technique for estimation of the instantaneous frequency based on simultaneous sampling of three-phase voltage signals is presented, which provides better performance, compared with the technique based on a single-phase signal in relation to waveforms with noise.
Abstract: A new technique for estimation of the instantaneous frequency based on simultaneous sampling of three-phase voltage signals is presented. The structure consists of two decoupled modules: the first is for adaptive filtering of input signals, and the second is for frequency estimation. A suitable and robust algorithm for frequency estimation is obtained. This technique provides better performance, compared with the technique based on a single-phase signal in relation to waveforms with noise. The technique is particularly important when asymmetric sags generate zero voltage in one of the three phases. In addition, it allows the measurement of the instantaneous frequency value of real signals for single- or three-phase systems. To demonstrate the performance of the developed algorithm, computer-simulated data records and calibrator-generated signals are processed. The proposed algorithm has been put to test with distorted three-phase voltage signals.
98 citations
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TL;DR: This work focuses on the local mode, the more commonly studied global mode, which preserves edges and details and is easily extensible to multi-channel data and results on color images include successful noise attenuation while preserving edges and detail by local mode filtering.
Abstract: Linear filters have two major drawbacks. First, edges in the image are smoothed with increasing filter size. Second, by extending the filters to multi-channel data, correlation between the channels is lost. Only a few researchers have explored the possibilities of mode filtering to overcome these problems. Mode filtering is motivated from both a local histogram with tonal scale and a robust statistics point of view. The tonal scale is proved to be equal to the scale of the error norm function within the robust statistics framework. Instead of the more commonly studied global mode, our focus is on the local mode. It preserves edges and details and is easily extensible to multi-channel data. A generalization of the spatial Gaussian filtering to a spatial and tonal Gaussian filter is used to iterate to the local mode. Results on color images include successful noise attenuation while preserving edges and detail by local mode filtering.
97 citations
Patent•
04 May 1976
TL;DR: In this paper, the authors identify and analyse the parameters of an input signal that contains speech in the presence of simultaneously occuring near-stationary noise, pauses between speech intervals as well as the termination of such noise can be recognized.
Abstract: By identifying and analyzing the properties of the parameters of an input signal that contains speech in the presence of simultaneously occuring near-stationary noise, pauses between speech intervals as well as the termination of such noise can be recognized. When a pause interval containing noise is recognized, the parameters identified during such interval are used to set the parameters of an adaptive filter through which the input signal is passed during subsequent intervals of speech and until the noise terminates. During the time the input signal passes through the filter, the near-stationary noise is filtered out. In response to recognition of the termination of noise, the input signal is caused to by-pass the filter which is then prepared to accept the parameters of noise occuring in a subsequent pause.
97 citations