Topic
Noise reduction
About: Noise reduction is a research topic. Over the lifetime, 25121 publications have been published within this topic receiving 300815 citations. The topic is also known as: denoising & noise removal.
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05 Jun 2000
TL;DR: This paper restricts its considerations to the case where only a single microphone recording of the noisy signal is available and proposes a method based on temporal quantiles in the power spectral domain, which is compared with pause detection and recursive averaging.
Abstract: Elimination of additive noise from a speech signal is a fundamental problem in audio signal processing. In this paper we restrict our considerations to the case where only a single microphone recording of the noisy signal is available. The algorithms which we investigate proceed in two steps. First, the noise power spectrum is estimated. A method based on temporal quantiles in the power spectral domain is proposed and compared with pause detection and recursive averaging. The second step is to eliminate the estimated noise from the observed signal by spectral subtraction or Wiener filtering. The database used in the experiments comprises 6034 utterances of German digits and digit strings by 770 speakers in 10 different cars. Without noise reduction, we obtain an error rate of 11.7%. Quantile based noise estimation and Wiener filtering reduce the error rate to 8.6%. Similar improvements are achieved in an experiment with artificial, non-stationary noise.
226 citations
01 Oct 2017
TL;DR: This paper proposes a multi-channel (MC) optimization model for real color image denoising under the weighted nuclear norm minimization (WNNM) framework, concatenate the RGB patches to make use of the channel redundancy, and introduces a weight matrix to balance the data fidelity of the three channels in consideration of their different noise statistics.
Abstract: Most of the existing denoising algorithms are developed for grayscale images. It is not trivial to extend them for color image denoising since the noise statistics in R, G, and B channels can be very different for real noisy images. In this paper, we propose a multi-channel (MC) optimization model for real color image denoising under the weighted nuclear norm minimization (WNNM) framework. We concatenate the RGB patches to make use of the channel redundancy, and introduce a weight matrix to balance the data fidelity of the three channels in consideration of their different noise statistics. The proposed MC-WNNM model does not have an analytical solution. We reformulate it into a linear equality-constrained problem and solve it via alternating direction method of multipliers. Each alternative updating step has a closed-form solution and the convergence can be guaranteed. Experiments on both synthetic and real noisy image datasets demonstrate the superiority of the proposed MC-WNNM over state-of-the-art denoising methods.
226 citations
TL;DR: A wavelet-based denoising technique for the recovery of signal contaminated by white additive Gaussian noise and a new thresholding procedure is proposed, called subband adaptive, which outperforms the existing thresholding techniques.
224 citations
TL;DR: It is shown that noise in the materialdensity images is negatively correlated and that this can be exploited for noise reduction in the two-basis material density images, and locally adaptive algorithms are presented.
Abstract: Dual-energy material density images obtained by prereconstruction-basis material decomposition techniques offer specific tissue information, but they exhibit relatively high pixel noise. It is shown that noise in the material density images is negatively correlated and that this can be exploited for noise reduction in the two-basis material density images. The algorithm minimizes noise-related differences between pixels and their local mean values, with the constraint that monoenergetic CT values, which can be calculated from the density images, remain unchanged. Applied to the material density images, a noise reduction by factors of 2 to 5 is achieved. While quantitative results for regions of interest remain unchanged, edge effects can occur in the processed images. To suppress these, locally adaptive algorithms are presented and discussed. Results are documented by both phantom measurements and clinical examples. >
224 citations
TL;DR: A brief introduction about CT imaging, the characteristics of noise in CT images and the popular methods of CT image denoising are presented and the merits and drawbacks of CT Image Denoising methods are discussed.
222 citations