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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.


Papers
More filters
Journal ArticleDOI
TL;DR: A decision-based, signal-adaptive median filtering algorithm for removal of impulse noise, which achieves accurate noise detection and high SNR measures without smearing the fine details and edges in the image.
Abstract: We propose a decision-based, signal-adaptive median filtering algorithm for removal of impulse noise. Our algorithm achieves accurate noise detection and high SNR measures without smearing the fine details and edges in the image. The notion of homogeneity level is defined for pixel values based on their global and local statistical properties. The cooccurrence matrix technique is used to represent the correlations between a pixel and its neighbors, and to derive the upper and lower bound of the homogeneity level. Noise detection is performed at two stages: noise candidates are first selected using the homogeneity level, and then a refining process follows to eliminate false detections. The noise detection scheme does not use a quantitative decision measure, but uses qualitative structural information, and it is not subject to burdensome computations for optimization of the threshold values. Empirical results indicate that our scheme performs significantly better than other median filters, in terms of noise suppression and detail preservation.

290 citations

Journal ArticleDOI
26 Jun 1989
TL;DR: In this paper, the switching pattern can be randomized by modulating the triangle carrier in sinusoidal PWM (pulse-width modulation) with bandlimited white noise, which can be used to avoid the concentration of harmonic energy in distinct tones.
Abstract: Acoustic noise in an inverter-driven AC electric machine can be reduced by avoiding the concentration of harmonic energy in distinct tones. One method to spread out the harmonic spectrum without the use of programmed PWM (pulse-width modulation) is to cause the switching pattern to be random. It is proposed that the switching pattern can be randomized by modulating the triangle carrier in sinusoidal PWM (pulse-width modulation) with bandlimited white noise. All the advantages of sinusoidal PWM are preserved with this technique. These include real-time control, linear operation, good transient response, and a constant average switching frequency. By controlling the bandwidth and RMS value of the pink noise modulation, it is shown that the instantaneous variation in switching frequency as well as the bandwidth of the energy spectrum in the machine can be specified within predetermined limits. Experimental results show the absence of acoustic noise concentrated at specific tones which is present with conventional sinusoidal modulation. >

290 citations

Journal ArticleDOI
TL;DR: A spatially adaptive two-dimensional wavelet filter is used to reduce speckle noise in time-domain and Fourier-domain optical coherence tomography (OCT) images.
Abstract: A spatially adaptive two-dimensional wavelet filter is used to reduce speckle noise in time-domain and Fourier-domain optical coherence tomography (OCT) images. Edges can be separated from discontinuities that are due to noise, and noise power can be attenuated in the wavelet domain without significantly compromising image sharpness. A single parameter controls the degree of noise reduction. When this filter is applied to ophthalmic OCT images, signal-to-noise ratio improvements of >7 dB are attained, with a sharpness reduction of <3%.

289 citations

Journal ArticleDOI
TL;DR: A novel denoising algorithm for photon-limited images which combines elements of dictionary learning and sparse patch-based representations of images and reveals that, despite its conceptual simplicity, Poisson PCA-based Denoising appears to be highly competitive in very low light regimes.
Abstract: Photon-limited imaging arises when the number of photons collected by a sensor array is small relative to the number of detector elements. Photon limitations are an important concern for many applications such as spectral imaging, night vision, nuclear medicine, and astronomy. Typically a Poisson distribution is used to model these observations, and the inherent heteroscedasticity of the data combined with standard noise removal methods yields significant artifacts. This paper introduces a novel denoising algorithm for photon-limited images which combines elements of dictionary learning and sparse patch-based representations of images. The method employs both an adaptation of Principal Component Analysis (PCA) for Poisson noise and recently developed sparsity-regularized convex optimization algorithms for photon-limited images. A comprehensive empirical evaluation of the proposed method helps characterize the performance of this approach relative to other state-of-the-art denoising methods. The results reveal that, despite its conceptual simplicity, Poisson PCA-based denoising appears to be highly competitive in very low light regimes.

289 citations

Journal ArticleDOI
TL;DR: A method called two-step noise reduction (TSNR) technique is proposed which solves this problem while maintaining the benefits of the decision-directed approach and a significant improvement is brought by HRNR compared to TSNR thanks to the preservation of harmonics.
Abstract: This paper addresses the problem of single-microphone speech enhancement in noisy environments. State-of-the-art short-time noise reduction techniques are most often expressed as a spectral gain depending on the signal-to-noise ratio (SNR). The well-known decision-directed (DD) approach drastically limits the level of musical noise, but the estimated a priori SNR is biased since it depends on the speech spectrum estimation in the previous frame. Therefore, the gain function matches the previous frame rather than the current one which degrades the noise reduction performance. The consequence of this bias is an annoying reverberation effect. We propose a method called two-step noise reduction (TSNR) technique which solves this problem while maintaining the benefits of the decision-directed approach. The estimation of the a priori SNR is refined by a second step to remove the bias of the DD approach, thus removing the reverberation effect. However, classic short-time noise reduction techniques, including TSNR, introduce harmonic distortion in enhanced speech because of the unreliability of estimators for small signal-to-noise ratios. This is mainly due to the difficult task of noise power spectrum density (PSD) estimation in single-microphone schemes. To overcome this problem, we propose a method called harmonic regeneration noise reduction (HRNR). A nonlinearity is used to regenerate the degraded harmonics of the distorted signal in an efficient way. The resulting artificial signal is produced in order to refine the a priori SNR used to compute a spectral gain able to preserve the speech harmonics. These methods are analyzed and objective and formal subjective test results between HRNR and TSNR techniques are provided. A significant improvement is brought by HRNR compared to TSNR thanks to the preservation of harmonics

286 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
20231,511
20222,974
20211,123
20201,488
20191,702
20181,631