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.
Papers published on a yearly basis
Papers
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TL;DR: A nonlinear temporal filtering algorithm using motion compensation for reducing noise in image sequences is shown to be successful in improving image quality and also improving the efficiency of subsequent image coding operations.
Abstract: Noise in television signals degrades both the image quality and the performance of image coding algorithms. This paper describes a nonlinear temporal filtering algorithm using motion compensation for reducing noise in image sequences. A specific implementation for NTSC composite television signals is described, and simulation results on several video sequences are presented. This approach is shown to be successful in improving image quality and also improving the efficiency of subsequent image coding operations.
222 citations
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TL;DR: Two short-time spectral amplitude estimators of the speech signal are derived based on a parametric formulation of the original generalized spectral subtraction method to improve the noise suppression performance of theoriginal method while maintaining its computational simplicity.
Abstract: In this paper, two short-time spectral amplitude estimators of the speech signal are derived based on a parametric formulation of the original generalized spectral subtraction method. The objective is to improve the noise suppression performance of the original method while maintaining its computational simplicity. The proposed parametric formulation describes the original method and several of its modifications. Based on the formulation, the speech spectral amplitude estimator is derived and optimized by minimizing the mean-square error (MSE) of the speech spectrum. With a constraint imposed on the parameters inherent in the formulation, a second estimator is also derived and optimized. The two estimators are different from those derived in most modified spectral subtraction methods, which are predominantly nonstatistical. When tested under stationary white Gaussian noise and semistationary Jeep noise, they showed improved noise suppression results.
220 citations
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TL;DR: An adaptive filtering approach based on discrete wavelet transform and artificial neural network is proposed for ECG signal noise reduction that can successfully remove a wide range of noise with significant improvement on SNR (signal-to-noise ratio).
219 citations
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TL;DR: The deep convolutional neural network (CNN) is introduced to achieve the HSI denoising method (HSI-DeNet), which can be regarded as a tensor-based method by directly learning the filters in each layer without damaging the spectral-spatial structures.
Abstract: The spectral and the spatial information in hyperspectral images (HSIs) are the two sides of the same coin. How to jointly model them is the key issue for HSIs’ noise removal, including random noise, structural stripe noise, and dead pixels/lines. In this paper, we introduce the deep convolutional neural network (CNN) to achieve this goal. The learned filters can well extract the spatial information within their local receptive filed. Meanwhile, the spectral correlation can be depicted by the multiple channels of the learned 2-D filters, namely, the number of filters in each layer. The consequent advantages of our CNN-based HSI denoising method (HSI-DeNet) over previous methods are threefold. First, the proposed HSI-DeNet can be regarded as a tensor-based method by directly learning the filters in each layer without damaging the spectral-spatial structures. Second, the HSI-DeNet can simultaneously accommodate various kinds of noise in HSIs. Moreover, our method is flexible for both single image and multiple images by slightly modifying the channels of the filters in the first and last layers. Last but not least, our method is extremely fast in the testing phase, which makes it more practical for real application. The proposed HSI-DeNet is extensively evaluated on several HSIs, and outperforms the state-of-the-art HSI-DeNets in terms of both speed and performance.
219 citations
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TL;DR: This work presents an optimal solution to the shadowing problem in the sense of least-mean-squares, which also provides an effective and convenient numerical method for noise reduction for data generated by a dynamical system.
219 citations