Median filter

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

Comparative approach for speckle reduction in medical ultrasound images

Abstract: Medical images are often deteriorated by noise due to various sources of interferences and other phenomena that affect the measurement processes in an imaging and acquisition system. Speckle noise is a random mottling of the image with bright and dark spots, which obscures fine details and degrades the detectability of low-contrast lesions. Speckle noise occurrence is often undesirable, since it affects the tasks of human interpretation and diagnosis. On the other hand, its texture carries important information about the tissue being imaged. Speckle filtering is thus a critical pre-processing step in medical ultrasound imagery, provided that the features of interest for diagnosis are not lost. In ultrasound images, the speckle energy is comparable to the signal energy in a wide range of frequency bands. Several speckle reduction techniques are applied to ultrasound images in order to reduce the noise level and improve the visual quality for better diagnoses. The optimum choice of wavelet bases for ultrasound images is investigated in this study. In order to realize a fair comparison, the same analysis for three frequency values is used. The comparison proves that the wavelet transform gives a much better result than both median filtering and homomorphic Wiener filtering methods for speckle reduction of ultrasound images.

Low residual noise speech enhancement utilizing time-frequency filtering

Abstract: Spectral subtraction is a well known technique for enhancing speech corrupted by additive wideband noise. In this technique, the "clean" signal is approximated by subtracting a noise estimate from the spectrum of the corrupted signal. A negative side effect is the residual "musical" noise that is produced when isolated spectral peaks exceed the noise estimate. In this paper, a low residual noise enhancement method is presented. This method is based on spectral subtraction but incorporates an algorithm developed to suppress "musical" noise without affecting speech. The algorithm is referred to as time-frequency filtering because spectral peaks due to noise are eliminated on the basis of duration, bandwidth, and proximity to other peaks. Results showing the effects of combining spectral subtraction and time-frequency filtering are given. >

Image data recursive noise filter with reduced temporal filtering of higher spatial frequencies

Abstract: An image data recursive noise filter wherein relatively high spatial frequency components of the image data are either not filtered at all or are filtered to a lesser degree than relatively low spatial frequency components of the image data. This minimizes blurring of fine low-contrast detail and also avoids "freezing" of noise in undetailed moving areas of the image.

A Noise Fading Technique for Images Highly Corrupted with Impulse Noise

Abstract: A novel noise fading technique based on noise detection and median filtering is proposed in this paper. This technique can be used for denoising the images extremely corrupted with impulse noise. This paper introduces a spike detection technique (SDT) and pixel restoring median filter (PRMF) for denoising the corrupted images. The SDT is used for discriminating between corrupted and uncorrupted image pixels. The corrupted pixels are restored using PRMF technique. Our iterative denoising technique is repeated until the corrupted pixels in the recovered image reduce to zero. The performance of our denoising scheme is evaluated with salt and pepper noise and also with random impulse noise for different standard images. It is observed that the proposed denoising scheme outperforms all existing impulse-denoising schemes. This technique can also be used for color image impulse noise removal. This technique can remove very high noise up to 98% and the images denoised with our method shows improvement in terms of visual quality, PSNR value and mutual information. This scheme prevents image blurring and is computationally simple. Hence it is suitable for real-time applications

Trajectory Preprocessing

Abstract: A spatial trajectory is a sequences of (x,y) points, each with a time stamp. This chapter discusses low-level preprocessing of trajectories. First, it discusses how to reduce the size of data required to store a trajectory, in order to save storage costs and reduce redundant data. The data reduction techniques can run in a batch mode after the data is collected or in an on-line mode as the data is collected. Part of this discussion consists of methods to measure the error introduced by the data reduction techniques. The second part of the chapter discusses methods for filtering spatial trajectories to reduce measurement noise and to estimate higher level properties of a trajectory like its speed and direction. The methods include mean and median filtering, the Kalman filter, and the particle filter.