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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TL;DR: An algorithm is described that removes the noise from images without causing blurring or other distortions of edges, using a new minimization strategy called mean-field annealing (MFA).
Abstract: An algorithm is described that removes the noise from images without causing blurring or other distortions of edges. The problem of noise removal is posed as a restoration of an uncorrupted image, given additive noise. The restoration problem is solved by using a new minimization strategy called mean-field annealing (MFA). An a priori statistical model of the image is chosen that drives the minimization toward solutions that are locally homogeneous. The strategy for MFA is derived, and the resulting algorithm is discussed. Applications of the algorithm to both synthetic images and real images are presented.
78 citations
TL;DR: This work addresses the problem of identifying the characterizing parameter of the PSF, which corresponds to motion or out-of-focus blur, from blurred and noisy images, by adding a preprocessing stage for noise reduction and applying an adaptive, quefrency-varying, comb-like window in the cepstral domain to enhance the blur parameter identification.
78 citations
18 Mar 2005
TL;DR: The concept of method noise is introduced, that is, the difference between the original (always slightly noisy) digital image and its denoised version, and some experiences comparing the NL-means results with some classical denoising methods are presented.
Abstract: In this work, we present and analyze an image denoising method, the NL-means algorithm, based on a non local averaging of all pixels in the image. We also introduce the concept of method noise, that is, the difference between the original (always slightly noisy) digital image and its denoised version. Finally, we present some experiences comparing the NL-means results with some classical denoising methods.
78 citations
TL;DR: In this paper, a non-coherent noise attenuation technique based on a constrained time-frequency transform is presented, which corresponds to a sparse representation of the microseismic signal in terms of a dictionary of complex Ricker wavelets.
Abstract: Noise contamination is an important problem in microseismic data processing, due to the low magnitude of the seismic events induced during fluid injection. In this study, a noncoherent noise attenuation technique based on a constrained time-frequency transform is presented. When applied to 1C data, the transform corresponds to a sparse representation of the microseismic signal in terms of a dictionary of complex Ricker wavelets. The use of complex wavelets possesses the advantage that signals with arbitrary phase can be represented with enhanced sparsity. A synthetic example illustrates the superior performance of the sparse constraint for denoising objectives when compared to the standard least-squares regularization. As the arrival time and frequency content of any wavefront are equivalent in the three components of a single receiver, the extension of the sparse transform to 3C data is accomplished when the three components are considered to share the same sparsity pattern in the time-frequency plane. A...
78 citations
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14 Aug 2012
TL;DR: By monitoring the wind noise in a location in which a cellular telephone is operating and by applying noise reduction and/or cancellation protocols at the appropriate time via analog and or digital signal processing, it is possible to significantly reduce wind noise entering into a communication system as discussed by the authors.
Abstract: By monitoring the wind noise in a location in which a cellular telephone is operating and by applying noise reduction and/or cancellation protocols at the appropriate time via analog and/or digital signal processing, it is possible to significantly reduce wind noise entering into a communication system.
78 citations