Median filter

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

Generalized selection weighted vector filters

Abstract: This paper introduces a class of nonlinear multichannel filters capable of removing impulsive noise in color images. The here-proposed generalized selection weighted vector filter class constitutes a powerful filtering framework for multichannel signal processing. Previously defined multichannel filters such as vector median filter, basic vector directional filter, directional-distance filter, weighted vector median filters, and weighted vector directional filters are treated from a global viewpoint using the proposed framework. Robust order-statistic concepts and increased degree of freedom in filter design make the proposed method attractive for a variety of applications. Introduced multichannel sigmoidal adaptation of the filter parameters and its modifications allow to accommodate the filter parameters to varying signal and noise statistics. Simulation studies reported in this paper indicate that the proposed filter class is computationally attractive, yields excellent performance, and is able to preserve fine details and color information while efficiently suppressing impulsive noise. This paper is an extended version of the paper by Lukac et al. presented at the 2003 IEEE-EURASIP Workshop on Nonlinear Signal and Image Processing (NSIP '03) in Grado, Italy.

Filter to reduce speckle artifact in ultrasound imaging

Abstract: A method for reducing speckle artifact in an ultrasound image using a two-dimensional median filter having a diamond-shaped five-point kernel. The entire pixel image data is passed through the filter in a manner such that the center point of the kernel is effectively stepped down each range vector in sequence. The magnitudes of the pixel data at each of the five points in the kernel are compared and the value which has the middle magnitude is adopted as a new pixel value, which is substituted for the old pixel value at the center point. After a new filtered vector has been formed from the new pixel values produced at successive center points by stepping down one acoustic vector, the kernel is shifted by one vector and stepped down range again. This process continues through the entire set of vectors until a new set of filtered vectors is formed. This filter will remove speckle holes on the order of one pixel in size while preserving good edge definition.

Reducing noise by repetition: introduction to signal averaging

Abstract: This paper describes theory and experiments, taken from biophysics and physiological measurements, to illustrate the technique of signal averaging. In the process, students are introduced to the basic concepts of signal processing, such as digital filtering, Fourier transformation, baseline correction, pink and Gaussian noise, and the cross- and autocorrelation functions. From these computations, the students estimate physically interesting parameters such as the pulse rate and blood flow velocity. They also learn about some of the pitfalls encountered in quantifying the signal and noise components for a meaningful computation of the signal-to-noise ratio.