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Noise measurement

About: Noise measurement is a research topic. Over the lifetime, 19776 publications have been published within this topic receiving 308180 citations.


Papers
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Proceedings ArticleDOI
11 Apr 1988
TL;DR: The author presents a self-adapting noise reduction system which is based on a four-microphone array combined with an adaptive postfiltering scheme which produces an enhanced speech signal with barely noticeable residual noise if the input SNR is greater than 0 dB.
Abstract: The author presents a self-adapting noise reduction system which is based on a four-microphone array combined with an adaptive postfiltering scheme. Noise reduction is achieved by utilizing the directivity gain of the array and by reducing the residual noise through postfiltering of the received microphone signals. The postfiltering scheme depends on a Wiener filter estimating the desired speech signal and is computed from short-term measurements of the autocorrelation and cross-correlation functions of the microphone signals. The noise reduction system has been tested experimentally in a typical office room. The system produces an enhanced speech signal with barely noticeable residual noise if the input SNR is greater than 0 dB. The received noise power-measured in the absence of the speech signal-can be reduced by 28 dB. >

370 citations

Proceedings ArticleDOI
17 Jun 2006
TL;DR: The utility of this noise estimation for two algorithms: edge detection and feature preserving smoothing through bilateral filtering for a variety of different noise levels is illustrated and good results are obtained for both these algorithms with no user-specified inputs.
Abstract: In order to work well, many computer vision algorithms require that their parameters be adjusted according to the image noise level, making it an important quantity to estimate. We show how to estimate an upper bound on the noise level from a single image based on a piecewise smooth image prior model and measured CCD camera response functions. We also learn the space of noise level functions how noise level changes with respect to brightness and use Bayesian MAP inference to infer the noise level function from a single image. We illustrate the utility of this noise estimation for two algorithms: edge detection and featurepreserving smoothing through bilateral filtering. For a variety of different noise levels, we obtain good results for both these algorithms with no user-specified inputs.

368 citations

Journal ArticleDOI
06 Jun 2004
TL;DR: In this paper, a modified derivative-superposition (DS) method was proposed to increase the maximum IIP3 at RF frequencies, which was used in a 0.25mum Si CMOS low-noise amplifier (LNA) designed for cellular code-division multiple access receivers.
Abstract: Intermodulation distortion in field-effect transistors (FETs) at RF frequencies is analyzed using the Volterra-series analysis. The degrading effect of the circuit reactances on the maximum IIP3 in the conventional derivative-superposition (DS) method is explained. The noise performance of this method is also analyzed and the effect of the subthreshold biasing of one of the FETs on the noise figure (NF) is shown. A modified DS method is proposed to increase the maximum IIP3 at RF. It was used in a 0.25-mum Si CMOS low-noise amplifier (LNA) designed for cellular code-division multiple-access receivers. The LNA achieved +22-dBm IIP3 with 15.5-dB gain, 1.65-dB NF, and 9.3 mA@2.6-V power consumption

366 citations

Journal ArticleDOI
TL;DR: In this paper, the authors investigated the noise introduced by the electron multiplication within the EMCCD and showed that the noise performance matches that of the ideal staircase avalanche photodiode, and a Monte Carlo method for simulating the low-light level images was demonstrated.
Abstract: Electron multiplying charge-coupled devices (EMCCDs) enable imaging with subelectron noise up to video frame rates and beyond, providing the multiplication gain is sufficiently high. The ultra-low noise, high resolution, high-quantum efficiency, and robustness to over exposure make these sensors ideally suited to applications traditionally served by image intensifiers. One important performance parameter of such low-light imaging systems is the noise introduced by the gain process. This work investigates the noise introduced by the electron multiplication within the EMCCD. The theory and measurements of the excess noise factor are presented. The measurement technique for determining the excess noise factor is described in detail. The results show that the noise performance matches that of the ideal staircase avalanche photodiode. A Monte Carlo method for simulating the low-light level images is demonstrated and the results compared with practical experience.

366 citations

Journal ArticleDOI
TL;DR: A novel Monte-Carlo technique is presented which enables the user to calculate SURE for an arbitrary denoising algorithm characterized by some specific parameter setting and it is demonstrated numerically that SURE computed using the new approach accurately predicts the true MSE for all the considered algorithms.
Abstract: We consider the problem of optimizing the parameters of a given denoising algorithm for restoration of a signal corrupted by white Gaussian noise. To achieve this, we propose to minimize Stein's unbiased risk estimate (SURE) which provides a means of assessing the true mean-squared error (MSE) purely from the measured data without need for any knowledge about the noise-free signal. Specifically, we present a novel Monte-Carlo technique which enables the user to calculate SURE for an arbitrary denoising algorithm characterized by some specific parameter setting. Our method is a black-box approach which solely uses the response of the denoising operator to additional input noise and does not ask for any information about its functional form. This, therefore, permits the use of SURE for optimization of a wide variety of denoising algorithms. We justify our claims by presenting experimental results for SURE-based optimization of a series of popular image-denoising algorithms such as total-variation denoising, wavelet soft-thresholding, and Wiener filtering/smoothing splines. In the process, we also compare the performance of these methods. We demonstrate numerically that SURE computed using the new approach accurately predicts the true MSE for all the considered algorithms. We also show that SURE uncovers the optimal values of the parameters in all cases.

365 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
202377
2022162
2021495
2020525
2019489
2018755