Median filter

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

Bio-potential signal processor for magnetic resonance imaging

Abstract: An adaptive filtering system is used to reduce the electrical noise on low power ECG signals and the like as generated by rapidly switched magnetic gradient fields in magnetic resonance imaging A correlated noise reference signal is derived from the inputs to the gradient coils by a combination differentiator and low pass filter The noise reference signal is received by a filter having adjustable coefficients and the result subtracted from the low power signal to produce an error signal used to adjust the coefficients of the filter Three separate adaptive filters may be placed in series each with a separate correlated noise reference signal to reduce the noise from three gradient coils

Evaluating noise filtering for event-based asynchronous change detection image sensors

Abstract: Bio-inspired Address Event Representation change detection image sensors, also known as silicon retinae, have matured to the point where they can be purchased commercially, and are easily operated by laymen. Noise is present in the output of these sensors, and improved noise filtering will enhance performance in many applications. A novel approach is proposed for quantifying the quality of data received from a silicon retina, and quantifying the performance of different noise filtering algorithms. We present a test rig which repetitively records printed test patterns, along with a method for averaging over repeated recordings to estimate the likelihood of an event being signal or noise. The calculated signal and noise probabilities are used to quantitatively compare the performance of 8 different filtering algorithms while varying each filter's parameters. We show how the choice of best filter and parameters varies as a function of the stimulus, particularly the temporal rate of change of intensity for a pixel, especially when the assumption of sharp temporal edges is not valid.

Apparatus and method for filtering digital image signal

Abstract: An apparatus and method of filtering a digital image signal. The apparatus includes: a noise reduction filter which selectively outputs one of results obtained by temporally and spatially filtering pixel values of pixels of each of frames of an image as a temporal or spatial filtering value in response to magnitudes of the results of temporal and spatial filtering; and a sharpness enhancement filter which highlights and outputs a high pass component of the temporal or spatial filtering value.

Comparative Evaluation of Ultrasound Kidney Image Enhancement Techniques

Abstract: Evaluation have been done to different enhancement techniques applied to ultrasound kidney images to see which enhancement techniques is the most suitable techniques that can be applied to the kidney images before segmenting the edge of the kidney. Five common enhancement techniques have been used including the spatial domain filtering, frequency domain filtering, histogram processing, morphological filtering and wavelet filtering. The techniques applied were assessed by few methods which are the observer sensitivity, measuring the image quality by calculating the MSE and PSNR of the image and applying one of the segmentation techniques to the output images. In conclusion, for ultrasound kidney image, if the whole image were taken into consideration (by measuring MSE and PSNR), morphological filtering seems to be the best option in enhancing the image. If the evaluator is concerning more on the kidney edges, enhancement techniques that should be taken into consideration are median filtering and histogram equalization.

An Amplitude-Preserved Time–Frequency Peak Filtering Based on Empirical Mode Decomposition for Seismic Random Noise Reduction

Abstract: Time-frequency peak filtering (TFPF) is a classical filtering method in time-frequency domain. It applies Wigner-Ville distribution to estimate the instantaneous frequency of an analytical signal. There is a pair of contradiction in this method, i.e., selecting a short window length may lead to good preservation for signal amplitude but bad random noise reduction whereas selecting a long window length may lead to serious attenuation for signal amplitude but effective random noise reduction. In order to make a good tradeoff between valid signal amplitude preservation and random noise reduction, we adopt empirical mode decomposition (EMD) to improve the TFPF results. The new idea is to utilize the decomposition characteristic of EMD which decomposes a signal to several modes from high to low frequency and to take advantage of the time-frequency filtering characteristic of TFPF which can recognize the valid signal component in the time-frequency plane in order to achieve effective random noise reduction together with good amplitude preservation. Through some experiments on synthetic seismic models and field seismic records, we show the better performance of the new method compared with the conventional TFPF.