Median filter

About: Median filter is a research topic. Over the lifetime, 12479 publications have been published within this topic receiving 178253 citations.

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. >

Toward edge sharpening: a SAR speckle filtering algorithm

Abstract: This paper makes two contributions. It first introduces an algorithm for synthetic aperture radar (SAR) speckle reduction and edge sharpening. Existing speckle filtering algorithms can effectively reduce the speckle effect but unfortunately also, to some degree, smear edges and blur images. Even for unfiltered images, there is still a need for edge sharpening; since SAR sensors have limited bandwidths, leading to slow responses to sudden changes (smearing sharp edges). The proposed algorithm functions as an adaptive-mean filter. Edge crossing points are detected by using the second-order derivative of the Gaussian function as a wavelet transform function. A proper dilation scale factor enables the wavelet transform function to detect only edge crossings and ignore the local oscillations. Then in a moving window the mean filter is applied if there is no edge crossing point. Otherwise, averaging is only applied to the part of the window separated by edge crossing points. Consequently, the algorithm smooths uniform areas while it sharpens and enhances edges. Edges of the filtered images are generally sharper than the original. Similarities between the proposed filter and other popular speckle filters, such as the Lee, Kuan, and Frost filters, designated for SAR multiplicative noise, are analyzed. Another contribution of the paper is the evaluation of popular filters from the viewpoint of texture preservation. The evaluation is carried out using the first and second-order histograms. Possible distortions caused by filters are explained.

Deblending using a space-varying median filter

Abstract: Deblending is a currently popular method for dealing with simultaneous-source seismic data. Removing blending noise while preserving as much useful signal as possible is the key to the deblending process. In this paper, I propose to use a space-varying median filter (SVMF) to remove blending noise. I demonstrate that this filtering method preserves more useful seismic reflection than does the conventional version of a median filter (MF). In SVMF, I use signal reliability (SR) as a reference to pick up the blending spikes and increase the window length in order to attenuate the spikes. When useful signals are identified, the window length is decreased in order to preserve more energy. The SR is defined as the local similarity between the data initially filtered using MF and the original noisy data. In this way, SVMF can be regionally adaptive, instead of rigidly using a constant window length through the whole profile for MF. Synthetic and field-data examples demonstrate excellent performance for my propos...

Maximum likelihood based noise covariance matrix estimation for multi-microphone speech enhancement

Abstract: Multi-microphone speech enhancement systems can often be decomposed into a concatenation of a beamformer, which provides spatial filtering of the noisy signal, and a singlechannel (SC) noise reduction filter, which reduces the noise remaining in the beamformer output. Here, we propose a maximum likelihood based method for estimating the intermicrophone covariance matrix of the noise impinging on the microphone array. The method allows prediction of this co-variance matrix for non-stationary noise sources even in signal regions where the target speech signal is present. Although the noise covariance matrix may have several purposes, we use it in this paper for estimating the power spectral density (psd) of the noise entering the SC filter, as this is important for optimal operation of the SC filter. In simulation experiments with a binaural hearing aid setup in a realistic acoustical scenario, the proposed method performs better than existing methods for estimating this noise psd.

Novel Hardware Implementation of Adaptive Median Filters

Abstract: A new FPGA implementation for adaptive median filters is proposed. Adaptive median filters exhibit better filtering properties than standard median filters; however, their implementation cost is higher. Proposed architecture was optimized for throughput allowing 300 M pixels to be filtered per second. The best performance/cost ratio exhibits the adaptive median filter which utilizes filtering window 7x7 pixels and can suppress shot noise with intensity up to 60%. In addition to filtering, adaptive median filters can be also used as detectors of corrupted pixels (detection statistics).