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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
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Journal ArticleDOI
TL;DR: In this article, a two-mode optical parametric oscillator operating above threshold was used to generate high-intensity twin beams which exhibit quantum correlations, and the noise power measured on the intensity difference between two such beams was reduced by 30% below the shot-noise limit.
Abstract: We have used a two-mode optical parametric oscillator operating above threshold to generate high-intensity twin beams which exhibit quantum correlations. The noise power measured on the intensity difference between two such beams is reduced by 30% below the shot-noise limit. Noise reduction is observed over a broad range of frequencies.

521 citations

Journal ArticleDOI
TL;DR: A hyperspectral image denoising algorithm employing a spectral-spatial adaptive total variation (TV) model, in which the spectral noise differences and spatial information differences are both considered in the process of noise reduction.
Abstract: The amount of noise included in a hyperspectral image limits its application and has a negative impact on hyperspectral image classification, unmixing, target detection, and so on In hyperspectral images, because the noise intensity in different bands is different, to better suppress the noise in the high-noise-intensity bands and preserve the detailed information in the low-noise-intensity bands, the denoising strength should be adaptively adjusted with the noise intensity in the different bands Meanwhile, in the same band, there exist different spatial property regions, such as homogeneous regions and edge or texture regions; to better reduce the noise in the homogeneous regions and preserve the edge and texture information, the denoising strength applied to pixels in different spatial property regions should also be different Therefore, in this paper, we propose a hyperspectral image denoising algorithm employing a spectral-spatial adaptive total variation (TV) model, in which the spectral noise differences and spatial information differences are both considered in the process of noise reduction To reduce the computational load in the denoising process, the split Bregman iteration algorithm is employed to optimize the spectral-spatial hyperspectral TV model and accelerate the speed of hyperspectral image denoising A number of experiments illustrate that the proposed approach can satisfactorily realize the spectral-spatial adaptive mechanism in the denoising process, and superior denoising results are produced

520 citations

Proceedings ArticleDOI
13 Jun 2010
TL;DR: The robustness and effectiveness of the proposed Denoising algorithm on removing mixed noise, e.g. heavy Gaussian noise mixed with impulsive noise, is validated in the experiments and the proposed approach compares favorably against some existing video denoising algorithms.
Abstract: Most existing video denoising algorithms assume a single statistical model of image noise, e.g. additive Gaussian white noise, which often is violated in practice. In this paper, we present a new patch-based video denoising algorithm capable of removing serious mixed noise from the video data. By grouping similar patches in both spatial and temporal domain, we formulate the problem of removing mixed noise as a low-rank matrix completion problem, which leads to a denoising scheme without strong assumptions on the statistical properties of noise. The resulting nuclear norm related minimization problem can be efficiently solved by many recently developed methods. The robustness and effectiveness of our proposed denoising algorithm on removing mixed noise, e.g. heavy Gaussian noise mixed with impulsive noise, is validated in the experiments and our proposed approach compares favorably against some existing video denoising algorithms.

516 citations

Journal ArticleDOI
TL;DR: From the results over synthetic and real images, it can be concluded that this recently proposed filter for random noise removal can be successfully used for automatic MR denoising.

510 citations

Journal ArticleDOI
TL;DR: After training on clean speech data, the performance of the recognizer was found to be severely degraded when noise was added to the speech signal at between 10 and 18 dB, but using PMC the performance was restored to a level comparable with that obtained when training directly in the noise corrupted environment.
Abstract: This paper addresses the problem of automatic speech recognition in the presence of interfering noise. It focuses on the parallel model combination (PMC) scheme, which has been shown to be a powerful technique for achieving noise robustness. Most experiments reported on PMC to date have been on small, 10-50 word vocabulary systems. Experiments on the Resource Management (RM) database, a 1000 word continuous speech recognition task, reveal compensation requirements not highlighted by the smaller vocabulary tasks. In particular, that it is necessary to compensate the dynamic parameters as well as the static parameters to achieve good recognition performance. The database used for these experiments was the RM speaker independent task with either Lynx Helicopter noise or Operation Room noise from the NOISEX-92 database added. The experiments reported here used the HTK RM recognizer developed at CUED modified to include PMC based compensation for the static, delta and delta-delta parameters. After training on clean speech data, the performance of the recognizer was found to be severely degraded when noise was added to the speech signal at between 10 and 18 dB. However, using PMC the performance was restored to a level comparable with that obtained when training directly in the noise corrupted environment.

509 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