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Noise reduction

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DIN-Buchsen haben, kann die Aussteuerung durch den Plus N55 allerdings etwas zu niedrig sein, da der Kompressor (Encoder)-Eingang 60 mV zur Vollaussteuerung benötigt und der Kompander selbst keine Signal-VerstĂ€rkung vornimmt. Die ebenfalls im gesamten Tonfrequenzbereich wirksamen Kompressor/Expander-Funktionen sind in zwei Frequenz-Bereiche aufgeteilt (f â‰ˆ 4,8 kHz), um jeweils ein optimales Arbeiten in diesen Bereichen zu gewĂ€hrleisten Die Kompander-Kennlinien des Super-D-Verfahrens veranschaulichen den Vorgang der wechselweisen Kompression und Expansion. Diese Kennlinien von Encoder und Decoder wurden bei den beiden Eingangspegeln 0 dB und −20 dB mit rosa Rauschen kontrolliert Da sich die Encoder/Decoder-Kennlinien hier schneiden, muß auch der Ausgangspegel des Decoders wieder O dB sein. Der Absenkungsgrad fĂŒr das Bandrauschen betrĂ€gt hier rd. 10 dB Wird ein Pegel von −20 dB eingespeist, hebt der Encoder diesen auf einen Ausgangspegel von −10 dB an Am Decoder Eingang liegt nun - vom BandgerĂ€t kommend ein Signalpegel von −10 dB, der nun gemeinsam mit dem Bandrauschen wieder um 10 dB auf den Ursprungswert herabgesetzt wird Geht das Encoder-Eingangssignal zum Beispiel auf −60 dB zurĂŒck, wird es auf −30 dB angehoben und auch wieder um 30 dB expandiert. So wird das Bandrauschen immer um den jeweiligen Kompressions/Expansionsgrad unterdrĂŒckt. "Über Alles" gesehen stellen sich bei jedem Eingangspegel lineare FrequenzgĂ€nge im gesamten Tonfrequenzbereich ein Das setzt allerdings voraus, daß die Kompressor- und Expander-Kennlinien bei Aufnahme und Wiedergabe deckungsgleich angesteuert werden. Man erreicht dieses mit einer Eichung ĂŒber den eingebauten Pegeltongenerator, wobei man den Ausschlag der Fluoreszenz-Anzeige am Plus N55 und am Aussteuerungsanzeiger des TonbandgerĂ€tes auf gleiche Werte (zum Beispiel −5 dB) einpegeln muß. Das ist ein einmaliger Vorgang bei gleichbleibender GerĂ€tekombination. Danach wird die Aufnahme nur noch am Kompander ausgesteuert. Beachtenswert sind noch die Verzerrungen, die durch das EinfĂŒgen einer ganzen Anzahl von Transistorstufen in den Übertragungsweg zusĂ€tzlich entstehen. Das Diagramm zeigt die frequenzabhĂ€ngigen Klirrfaktoren bei Vollaussteuerung der beiden Encoder- und Decoder-Strecken im Plus N55. Im Vergleich zu linearen VerstĂ€rkern sind sie relativ hoch, gegenĂŒber den im Bereich der Vollaussteuerung vorliegenden kubischen Klirrfaktoren bei Cassetten-BĂ€ndern aber noch vertretbar.
634:. Noise can therefore be also removed by use of spectral editing tools, which work in this time-frequency domain, allowing local modifications without affecting nearby signal energy. This can be done manually much like in a paint program drawing pictures. Another way is to define a dynamic threshold for filtering noise, that is derived from the local signal, again with respect to a local time-frequency region. Everything below the threshold will be filtered, everything above the threshold, like partials of a voice or 155: 143:, inversion, and interpretation, thereby greatly improving the success rate in oil & gas exploration. The useful signal that is smeared in the ambient random noise is often neglected and thus may cause fake discontinuity of seismic events and artifacts in the final migrated image. Enhancing the useful signal while preserving edge properties of the seismic profiles by attenuating random noise can help reduce interpretation difficulties and misleading risks for oil and gas detection. 893:
image information is concentrated in a few large ones. Therefore, the first wavelet-based denoising methods were based on thresholding of detail subband coefficients. However, most of the wavelet thresholding methods suffer from the drawback that the chosen threshold may not match the specific distribution of signal and noise components at different scales and orientations.
1207: 172: 174: 115:, noise (both visible and audible) is introduced due to the grain structure of the medium. In photographic film, the size of the grains in the film determines the film's sensitivity, more sensitive film having larger-sized grains. In magnetic tape, the larger the grains of the magnetic particles (usually 896:
To address these disadvantages, nonlinear estimators based on Bayesian theory have been developed. In the Bayesian framework, it has been recognized that a successful denoising algorithm can achieve both noise reduction and feature preservation if it employs an accurate statistical description of the
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It used a root-mean-squared (RMS) encode/decode algorithm with the noise-prone high frequencies boosted, and the entire signal fed through a 2:1 compander. dbx operated across the entire audible bandwidth and unlike Dolby B was unusable without a decoder. However, it could achieve up to 30 dB
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in 1966. Intended for professional use, Dolby Type A was an encode/decode system in which the amplitude of frequencies in four bands was increased during recording (encoding), then decreased proportionately during playback (decoding). In particular, when recording quiet parts of an audio signal, the
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A median filter is a rank-selection (RS) filter, a particularly harsh member of the family of rank-conditioned rank-selection (RCRS) filters; a much milder member of that family, for example one that selects the closest of the neighboring values when a pixel's value is external in its neighborhood,
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The main aim of an image denoising algorithm is to achieve both noise reduction and feature preservation using the wavelet filter banks. In this context, wavelet-based methods are of particular interest. In the wavelet domain, the noise is uniformly spread throughout coefficients while most of the
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AI Image Denoiser is much more aggressive, significantly enhancing details, but also applying heavy smoothing. DxO PureRAW, which directly improves the raw image using deep learning trained on "millions of images analyzed by DxO over 15 years," was easily the most effective of the many denoisers
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Super-Dolby im Plus N 55 Der Kompander "Plus N55" arbeitet nach dem von Sanyo entwickelten Super-D-Noise-Reduction-System. Er ist speziell fĂŒr 3-Kopf-GerĂ€te konzipiert und den PegelverhĂ€ltnissen von japanischen Cassetten-BandgerĂ€ten angepaßt. FĂŒr Hi-Fi-Anlagen, die ausschließlich
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in the image are very different in color or intensity from their surrounding pixels; the defining characteristic is that the value of a noisy pixel bears no relation to the color of surrounding pixels. When viewed, the image contains dark and white dots, hence the term salt and pepper noise.
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frequencies above 1 kHz would be boosted. This had the effect of increasing the signal-to-noise ratio on tape up to 10 dB depending on the initial signal volume. When it was played back, the decoder reversed the process, in effect reducing the noise level by up to 10 dB.
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broadcasts, reducing noise by as much as 10 dB. They can also be used in conjunction with other noise reduction systems, provided that they are used prior to applying DNR to prevent DNR from causing the other noise reduction system to mistrack.
1257:. One method of denoising that uses the auto-normal model uses the image data as a Bayesian prior and the auto-normal density as a likelihood function, with the resulting posterior distribution offering a mean or mode as a denoised image. 792:. This convolution brings the value of each pixel into closer harmony with the values of its neighbors. In general, a smoothing filter sets each pixel to the average value, or a weighted average, of itself and its nearby neighbors; the 849:
averaging of all the pixels in an image. In particular, the amount of weighting for a pixel is based on the degree of similarity between a small patch centered on that pixel and the small patch centered on the pixel being de-noised.
173: 772:). Most photographic noise reduction algorithms split the image detail into chroma and luminance components and apply more noise reduction to the former or allows the user to control chroma and luminance noise reduction separately. 171: 2718:
Mehdi Mafi, Harold Martin, Jean Andrian, Armando Barreto, Mercedes Cabrerizo, Malek Adjouadi, "A Comprehensive Survey on Impulse and Gaussian Denoising Filters for Digital Images", Signal Processing, vol. 157, pp. 236–260,
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of identical size. Stacks of similar macroblocks are then filtered together in the transform domain and each image fragment is finally restored to its original location using a weighted average of the overlapping pixels.
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noise reduction systems, DNL and DNR are playback-only signal processing systems that do not require the source material to first be encoded. They can be used to remove background noise from any audio signal, including
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Median and other RCRS filters are good at removing salt and pepper noise from an image, and also cause relatively little blurring of edges, and hence are often used in computer vision applications.
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the available computer power and time available: a digital camera must apply noise reduction in a fraction of a second using a tiny onboard CPU, while a desktop computer has much more power and time
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Forouzanfar, M.; Abrishami-Moghaddam, H.; Ghadimi, S. (July 2008). "Locally adaptive multiscale Bayesian method for image denoising based on bivariate normal inverse Gaussian distributions".
1202:{\displaystyle \mathbb {P} {\big (}x(i)=c\mid x(j)\,\forall j\in \delta _{i}{\big )}\propto \exp \left({-{\frac {\beta }{2\lambda }}\sum _{j\in \delta _{i}}{\big (}c-x(j){\big )}^{2}}\right)} 803:
across the area. Because of this blurring, linear filters are seldom used in practice for noise reduction; they are, however, often used as the basis for nonlinear noise reduction filters.
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will pick up noise from a variety of sources. Further use of these images will often require that the noise be reduced either for aesthetic purposes, or for practical purposes such as
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use frequency modulation for the luminance part (composite video signal in direct color systems), which keeps the tape at saturation level, audio-style noise reduction is unnecessary.
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Noise reduction algorithms tend to alter signals to a greater or lesser degree. The local signal-and-noise orthogonalization algorithm can be used to avoid changes to the signals.
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Chen, Yangkang; Yuan, Jiang; Zu, Shaohuan; Qu, Shan; Gan, Shuwei (2015). "Seismic imaging of simultaneous-source data using constrained least-squares reverse time migration".
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noise reduction systems have a pre-emphasis process applied during recording and then a de-emphasis process applied at playback. Systems include the professional systems
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Modern digital sound recordings no longer need to worry about tape hiss so analog-style noise reduction systems are not necessary. However, an interesting twist is that
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In some compander systems, the compression is applied during professional media production and only the expansion is applied by the listener; for example, systems like
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transceivers, etc. Both of the aforementioned filters can be used separately, or in conjunction with each other at the same time, depending on the transceiver itself.
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whether sacrificing some real detail is acceptable if it allows more noise to be removed (how aggressively to decide whether variations in the image are noise or not)
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Dabov, Kostadin; Foi, Alessandro; Katkovnik, Vladimir; Egiazarian, Karen (16 July 2007). "Image denoising by sparse 3D transform-domain collaborative filtering".
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Generally, this type of noise will only affect a small number of image pixels. Typical sources include flecks of dust inside the camera and overheated or faulty
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Autocorrelator Noise Reduction and Dynamic Range Recovery System (Models 1000 and 4000) can reduce various noise from old recordings. Dual-ended systems (such as
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Huang, Weilin; Wang, Runqiu; Chen, Yangkang; Li, Huijian; Gan, Shuwei (2016). "Damped multichannel singular spectrum analysis for 3D random noise attenuation".
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created by random electron motion due to thermal agitation. These agitated electrons rapidly add and subtract from the output signal and thus create detectable
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Funk-Technik - Fachzeitschrift fĂŒr Funk-Elektroniker und Radio-Fernseh-Techniker - Offizielles Mitteilungsblatt der Bundesfachgruppe Radio- und Fernsehtechnik
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In either case, the noise at different pixels can be either correlated or uncorrelated; in many cases, noise values at different pixels are modeled as being
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Smoothing filters tend to blur an image because pixel intensity values that are significantly higher or lower than the surrounding neighborhood
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There are many noise reduction algorithms in image processing. In selecting a noise reduction algorithm, one must weigh several factors:
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greyscale value is normally distributed with mean equal to the average greyscale value of its neighboring pixels and a given variance.
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Chen, Yangkang; Fomel, Sergey (November–December 2015). "Random noise attenuation using local signal-and-noise orthogonalization".
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is an example of a nonlinear filter and, if properly designed, is very good at preserving image detail. To run a median filter:
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of noise. While other distributions are possible, the Gaussian (normal) distribution is usually a good model, due to the
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Boashash, B. (April 1992). "Estimating and Interpreting the Instantaneous Frequency of a Signal-Part I: Fundamentals".
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2022 3rd International Conference on Big Data, Artificial Intelligence and Internet of Things Engineering (ICBAIE)
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is the ability of a circuit to isolate an undesired signal component from the desired signal component, as with
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Chen, Yangkang; Ma, Jianwei; Fomel, Sergey (2016). "Double-sparsity dictionary for seismic noise attenuation".
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Most general-purpose image and photo editing software will have one or more noise-reduction functions (median,
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In real-world photographs, the highest spatial-frequency detail consists mostly of variations in brightness (
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Four types of noise reduction exist: single-ended pre-recording, single-ended hiss reduction, single-ended
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and leaves it unchanged otherwise, is sometimes preferred, especially in photographic applications.
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functionality worked not only for playback, but, as an undocumented feature, also during recording.
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Chen, Yangkang (2017). "Probing the subsurface karst features using time-frequency decomposition".
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Seyyedi, Saeed (2018). "Incorporating a Noise Reduction Technique Into X-Ray Tensor Tomography".
2539: 1380: 919: 909:, one can model the pixels in a greyscale image as auto-normally distributed, where each pixel's 181: 3755: 3690: 3653: 3132: 2566:
Banerjee, Shounak; Sarkar, Debarpito; Chatterjee, Debraj; Chowdhuri, Sunanda Roy (2021-06-25).
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HIGH COM - The HIGH COM broadband compander utilizing the U401BR integrated circuit
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yet requires much lower computational overhead such that it can be performed directly within
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Ulyanov, Dmitry; Vedaldi, Andrea; Lempitsky, Victor (30 November 2017). "Deep Image Prior".
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the characteristics of the noise and the detail in the image, to better make those decisions
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or smoothing operation. For example, the Gaussian mask comprises elements determined by a
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reduction, and codec or dual-ended systems. Single-ended pre-recording systems (such as
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that says that the sum of different noises tends to approach a Gaussian distribution.
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systems in US GM cars introduced in 1984. It was also used in factory car stereos in
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Matlab software and Photoshop plug-in for image denoising (Pointwise SA-DCT filter)
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Orazaev, Anzor; Lyakhov, Pavel; Baboshina, Valentina; Kalita, Diana (2023-01-26).
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Matlab software for image and video denoising (Non-local transform-domain filter)
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International Journal of Wavelets, Multiresolution and Information Processing
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http://www.hellodirect.com/catalog/Product.jhtml?PRODID=11127&CATID=15295
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Gan, Shuwei; Wang, Shoudong; Chen, Yangkang; Qu, Shan; Zu, Shaohuan (2016).
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Intelligent Robots and Computer Vision XIII: Algorithms and Computer Vision
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Burwen, Richard S. (December 1971). "Design of a Noise Eliminator System".
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Another method for removing noise is to evolve the image under a smoothing
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2018 International Symposium on Electronics and Telecommunications (ISETC)
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A second class of algorithms work in the time-frequency domain using some
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Time-Frequency Signal Analysis and Processing – A Comprehensive Reference
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The first widely used audio noise reduction technique was developed by
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to address scratches, pops, and surface non-linearities. Single-ended
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Craciun, G.; Jiang, Ming; Thompson, D.; Machiraju, R. (March 2005).
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Dong, Suge; Dong, Chunxiao; Li, Zishuang; Ge, Mingtao (2022-07-15).
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sort the neighbouring pixels into order based upon their intensities
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Journal of the Royal Statistical Society. Series B (Methodological)
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Chen, Yangkang; Chen, Hanming; Xiang, Kui; Chen, Xiaohong (2017).
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Xue, Zhiguang; Chen, Yangkang; Fomel, Sergey; Sun, Junzhe (2016).
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approaches have been proposed to achieve noise reduction and such
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Chervyakov, N. I.; Lyakhov, P. A.; Nagornov, N. N. (2018-11-01).
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2021 International Conference on Intelligent Technologies (CONIT)
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Haase, Hans-Joachim (August 1980). Written at Aschau, Germany.
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can be applied to group similar image fragments of overlapping
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systems actually add noise to a signal to improve its quality.
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ExKo Breitband-Kompander Aufnahme/Wiedergabe 9 dB Tonband
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Burwen, Richard S. (February 1971). "A Dynamic Noise Filter".
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Non-local image denoising, with code and online demonstration
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Gajitzki, Paul; Isar, Dorina; Simu, Călin (November 2018).
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and is notable in that it requires no prior training data.
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Statistical methods for image denoising exist as well. For
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was a competing analog noise reduction system developed by
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Boosting signals in seismic data is especially crucial for
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101/323/323A/323AA and 325) is applied to the playback of
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and MXR Innovations' MXR as well as the consumer systems
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IEEE Transactions on Visualization and Computer Graphics
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technique that brings performance comparable to that of
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Yet another approach is the automatic noise limiter and
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frequency smoothing, and 0.15 seconds attack/decay time.
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R., C. (1965). "Kompander verbessert Magnettonkopie".
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One of DNR's first widespread applications was in the
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that have local characteristics and are often called
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Optoelectronics, Instrumentation and Data Processing
1882: 1249: 1229: 1201: 1011: 991: 955: 935: 472:The Dolby B system (developed in conjunction with 1921:(UK) – up-to-date electronics for lab and leisure 1591: 872:replace the original value of the pixel with the 784:the original image with a mask that represents a 526:Dynamic noise limiter and dynamic noise reduction 4134: 3179:Shrinkage Fields for Effective Image Restoration 2957: 2955: 2902: 2731:"Fuzzy neural networks: Theory and applications" 2116: 2114: 2112: 2110: 2108: 2106: 2104: 845:Another approach for removing noise is based on 3018:"On the Statistical Analysis of Dirty Pictures" 2251: 1951:Audio Noise Suppressor Model 325 Owner's Manual 1700: 1515: 4007: 3330: 2952: 2101: 2052: 2014: 1995: 1632: 1183: 1157: 1095: 1035: 2662: 2288: 2170:(NB. Page 736 is missing in the linked PDF.) 2071: 1942: 1556: 1260: 669:have one or more noise reduction functions. 3175: 2417:"LM1894 Dynamic Noise Reduction System DNR" 1776: 775: 545:. Its circuitry is also based on a single 30:For the reduction of a sound's volume, see 4014: 4000: 3337: 3323: 3074:IEEE Transactions on Computational Imaging 2487:, Vol. 21, No. 6, September/October 2005.) 2173: 1874:|...|intentional=yes}} 3270: 3245: 3136: 2639: 2182:"RauschunterdrĂŒckung: Kampf dem Rauschen" 1837: 1761: 1658: 1617: 1541: 1472: 1073: 1029: 134: 2536: 2507: 2120: 2061:Journal of the Audio Engineering Society 2004:Journal of the Audio Engineering Society 1976: 1333:is one such technique that makes use of 806: 564:to reduce noise levels on long-distance 188:, 5 dB, 12 dB, and 30 dB reduction, 150 3813:Signal-to-interference-plus-noise ratio 3071: 2294: 2077: 1357: 730:independent and identically distributed 282:Compander-based noise reduction systems 27:Process of removing noise from a signal 14: 4135: 3629:Equivalent pulse code modulation noise 2996: 2671:. Xi’an, China: IEEE. pp. 24–27. 2058: 2020: 2001: 900: 492:could be utilized to work as a mostly 3995: 3318: 3227: 3117:IEEE Transactions on Image Processing 3015: 2728: 2179: 1729: 1626: 1550: 1509: 796:is just one possible set of weights. 760:Chroma and luminance noise separation 4021: 3752:(energy per symbol to noise density) 2999:A Wavelet Tour of Signals Processing 2574:. Hubli, India: IEEE. pp. 1–6. 2226: 1811: 1735: 1694: 1673: 1466: 1290:Shrinkage Fields (image restoration) 887: 853: 656: 3290:Recent trends in denoising tutorial 3176:Schmidt, Uwe; Roth, Stefan (2014). 2199:. pp. W293–W296, W298, W300 . 1805: 1667: 1447: 1420: 617: 610:vehicles in the 1980s, such as the 212:is a performance-limiting issue in 24: 3823:Signal-to-quantization-noise ratio 3037:10.1111/j.2517-6161.1986.tb01412.x 2301:. Vol. 1 (revised ed.). 2266:(Semiconductor information 2.80). 1982: 1770: 1585: 1074: 943:denote the pixels adjacent to the 834: 153: 25: 4169: 3737:(energy per bit to noise density) 3705:Carrier-to-receiver noise density 3619:Effective input noise temperature 3283: 2729:Liu, Puyin; Li, Hongxing (2004). 2465: 1818:Geophysical Journal International 1742:Geophysical Journal International 1639:Geophysical Journal International 1598:Geophysical Journal International 967:of the greyscale intensity (on a 780:One method to remove noise is by 768:) rather than variations in hue ( 180:Example of noise reduction using 4153:Image noise reduction techniques 3348:(physics and telecommunications) 2677:10.1109/ICBAIE56435.2022.9985814 2342:"Philips' Dynamic Noise Limiter" 2130:radio fernsehen elektronik (rfe) 2021:Burwen, Richard S. (June 1971). 1316: 866:consider each pixel in the image 426:, and the Hungarian/East-German 200:Problems playing this file? See 169: 3960:Block-matching and 3D filtering 3808:Signal-to-noise ratio (imaging) 3659:Noise, vibration, and harshness 3258: 3247:10.2352/EI.2022.34.14.COIMG-151 3221: 3210:from the original on 2018-01-02 3169: 3108: 3065: 3054:from the original on 2017-08-29 3009: 2990: 2941:from the original on 2021-04-21 2896: 2885:from the original on 2021-04-21 2830: 2775: 2733:. In Casasent, David P. (ed.). 2722: 2712: 2701:from the original on 2022-12-25 2656: 2615: 2604:from the original on 2021-08-10 2580:10.1109/CONIT51480.2021.9498402 2559: 2530: 2501: 2490: 2474:from the original on 2008-07-05 2459: 2434: 2409: 2384: 2359: 2334: 2323:from the original on 2008-05-13 2309: 2277:from the original on 2016-04-16 2240:from the original on 2021-04-26 2211:from the original on 2021-05-16 2158:from the original on 2021-05-05 2090:from the original on 2020-10-28 2078:Lambert, Mel (September 1978). 2041:from the original on 2017-11-13 2023:"110 dB Dynamic Range For Tape" 1964:from the original on 2021-05-05 1930:from the original on 2020-07-02 1307:Block-matching and 3D filtering 1283: 1267:Block-matching and 3D filtering 461:used in FM radio broadcasting. 3295:Noise Reduction in photography 2470:. Riviera Owners Association. 2319:. Audiotools.com. 2013-11-10. 2298:Encyclopedia of Recorded Sound 1177: 1171: 1070: 1064: 1049: 1043: 986: 974: 552:It was further developed into 449:used for vinyl recordings and 13: 1: 3493:Additive white Gaussian noise 2483:(NB. Originally published in 2190:Dr. Alfred HĂŒthig Verlag GmbH 1579:10.1016/j.jappgeo.2015.01.004 1559:Journal of Applied Geophysics 1459: 897:signal and noise components. 813:partial differential equation 126: 3869:Interference (communication) 3776:Signal-to-interference ratio 3766:Signal, noise and distortion 2369:. ComPol Inc. Archived from 1335:convolutional neural network 1230:{\displaystyle \beta \geq 0} 740: 672: 570:Dolby noise-reduction system 265:Dolby noise-reduction system 7: 3624:Equivalent noise resistance 1410:Noise-cancelling headphones 1352: 1340: 936:{\displaystyle \delta _{i}} 243:and the earlier SAE 5000A, 146: 96:, a major type of noise is 64:common-mode rejection ratio 58:the signal to some degree. 46:is the process of removing 10: 4174: 2917:10.1109/ISETC.2018.8583929 2508:Boashash, B., ed. (2003). 2295:Hoffman, Frank W. (2004). 1364:Filter (signal processing) 1287: 1264: 838: 735: 732:, and hence uncorrelated. 663:digital audio workstations 29: 4074: 4029: 3932: 3919:Total variation denoising 3901: 3892: 3846: 3683: 3599: 3485: 3409: 3353: 2976:10.1142/S0219691308002562 2808:10.3103/S8756699018060092 2367:"Dynamic Noise Reduction" 1437:Total variation denoising 1261:Block-matching algorithms 18:Pumping (noise reduction) 4102:Super-resolution imaging 3086:10.1109/TCI.2018.2794740 2184:. Systeme und Konzepte. 2121:Bergmann, Heinz (1982). 1432:Digital image processing 1374: 1273:block-matching algorithm 1250:{\displaystyle \lambda } 965:conditional distribution 776:Linear smoothing filters 692: 236: 232: 3833:Contrast-to-noise ratio 3147:10.1109/TIP.2007.901238 2540:Proceedings of the IEEE 2218:. pp. W298, W300: 1812:Chen, Yangkang (2017). 1736:Chen, Yangkang (2016). 1688:10.1190/INT-2016-0030.1 1381:Architectural acoustics 1211:for a chosen parameter 554:dynamic noise reduction 320:Burwen Noise Eliminator 257:dynamic range expanders 165:Noise reduction example 3756:Modulation error ratio 3691:Carrier-to-noise ratio 3654:Noise spectral density 3016:Besag, Julian (1986). 1799:10.1190/geo2014-0525.1 1723:10.1190/geo2015-0264.1 1543:10.1190/geo2014-0524.1 1495:10.1190/GEO2014-0227.1 1427:Dark-frame subtraction 1251: 1231: 1203: 1013: 993: 957: 937: 667:audio editing software 632:time-frequency filters 562:National Semiconductor 455:High Com FM 158: 135:In seismic exploration 3971:Denoising autoencoder 3945:Anisotropic diffusion 3790:Signal-to-noise ratio 3634:Impulse noise (audio) 3549:Johnson–Nyquist noise 3437:Government regulation 3228:Dietz, Henry (2022). 3191:10.1109/CVPR.2014.349 2143:. pp. 731–736 . 1866:|...}} 1845:(Retracted, see 1252: 1232: 1204: 1014: 994: 958: 938: 821:anisotropic diffusion 807:Anisotropic diffusion 723:central limit theorem 699:salt and pepper noise 531:Dynamic noise limiter 214:analog tape recording 157: 3854:List of noise topics 2855:10.1109/TVCG.2005.35 2303:Taylor & Francis 1395:Codec listening test 1358:General noise issues 1349:, despeckle, etc.). 1241: 1215: 1025: 1003: 971: 947: 920: 515:of noise reduction. 3614:Circuit noise level 3609:Channel noise level 3129:2007ITIP...16.2080D 2997:Mallat, S. (1998). 2800:2018OIDP...54..608C 2743:1994SPIE.2353..303G 2641:10.3390/app13031585 2084:Sound International 1851:10.1093/gji/ggaa256 1830:2017GeoJI.209...21C 1791:2016Geop...81V.193C 1754:2016GeoJI.206..457C 1715:2016Geop...81V.261H 1651:2016GeoJI.204..768G 1610:2016GeoJI.207.1313C 1571:2015JAG...114...32C 1534:2016Geop...81S..11X 1487:2015Geop...80D...1C 963:th pixel. Then the 901:Statistical methods 876:value from the list 719:normal distribution 541:in 1971 for use on 486:integrated circuit 4075:Special processing 3670:Pseudorandom noise 3560:Quantization error 3371:Noise cancellation 3234:Electronic Imaging 2134:VEB Verlag Technik 1839:10.1093/gji/ggw492 1763:10.1093/gji/ggw165 1660:10.1093/gji/ggv484 1619:10.1093/gji/ggw343 1247: 1227: 1199: 1154: 1009: 989: 953: 933: 829:Gaussian filtering 819:, which is called 677:Images taken with 645:commonly found on 300:Dolby Laboratories 253:phonograph records 159: 94:electronic systems 4148:Audio engineering 4130: 4129: 4117:Pixel art scaling 4082:Film colorization 3989: 3988: 3985: 3984: 3924:Wavelet denoising 3884:Thermal radiation 3879:Spectrum analyzer 3675:Statistical noise 3499:Atmospheric noise 3396:Noise temperature 3381:Noise measurement 3361:Acoustic quieting 3200:978-1-4799-5118-5 2926:978-1-5386-5925-0 2760:978-981-238-786-8 2751:10.1117/12.188903 2686:978-1-6654-5160-4 2589:978-1-7281-8583-5 2523:978-0-08-044335-5 2317:"Noise Reduction" 1327:image restoration 1132: 1130: 1012:{\displaystyle i} 956:{\displaystyle i} 888:Wavelet transform 854:Nonlinear filters 790:Gaussian function 657:Software programs 628:nonlinear filters 575:Unlike Dolby and 508:David E. Blackmer 314:, Donald Aldous' 175: 109:photographic film 71:signal processing 16:(Redirected from 4165: 4023:Video processing 4016: 4009: 4002: 3993: 3992: 3977:Deep Image Prior 3966:Shrinkage Fields 3950:Bilateral filter 3899: 3898: 3504:Background noise 3401:Phase distortion 3339: 3332: 3325: 3316: 3315: 3277: 3276: 3274: 3262: 3256: 3255: 3249: 3225: 3219: 3218: 3216: 3215: 3209: 3184: 3173: 3167: 3166: 3140: 3123:(8): 2080–2095. 3112: 3106: 3105: 3069: 3063: 3062: 3060: 3059: 3053: 3022: 3013: 3007: 3006: 2994: 2988: 2987: 2959: 2950: 2949: 2947: 2946: 2911:. pp. 1–4. 2900: 2894: 2893: 2891: 2890: 2834: 2828: 2827: 2779: 2773: 2772: 2726: 2720: 2716: 2710: 2709: 2707: 2706: 2660: 2654: 2653: 2643: 2628:Applied Sciences 2619: 2613: 2612: 2610: 2609: 2563: 2557: 2556: 2553:10.1109/5.135376 2534: 2528: 2527: 2514:Elsevier Science 2505: 2499: 2494: 2488: 2482: 2480: 2479: 2463: 2457: 2456: 2454: 2453: 2444:. Archived from 2438: 2432: 2431: 2429: 2428: 2419:. Archived from 2413: 2407: 2406: 2404: 2403: 2394:. Archived from 2388: 2382: 2381: 2379: 2378: 2363: 2357: 2356: 2354: 2353: 2344:. Archived from 2338: 2332: 2331: 2329: 2328: 2313: 2307: 2306: 2292: 2286: 2285: 2283: 2282: 2276: 2265: 2255: 2249: 2248: 2246: 2245: 2230: 2224: 2223: 2217: 2216: 2198: 2177: 2171: 2169: 2164: 2163: 2157: 2142: 2127: 2118: 2099: 2098: 2096: 2095: 2075: 2069: 2068: 2056: 2050: 2049: 2047: 2046: 2040: 2027: 2018: 2012: 2011: 1999: 1993: 1992: 1980: 1974: 1972: 1970: 1969: 1963: 1956: 1946: 1940: 1938: 1936: 1935: 1929: 1915: 1907: 1880: 1879: 1877: 1875: 1867: 1856:Retraction Watch 1843: 1841: 1809: 1803: 1802: 1785:(4): V261–V270. 1774: 1768: 1767: 1765: 1733: 1727: 1726: 1709:(4): V261–V270. 1698: 1692: 1691: 1682:(4): T533–T542. 1671: 1665: 1664: 1662: 1630: 1624: 1623: 1621: 1589: 1583: 1582: 1554: 1548: 1547: 1545: 1513: 1507: 1506: 1470: 1448:Similar problems 1421:Images and video 1331:Deep Image Prior 1311:embedded systems 1303:machine learning 1295:Shrinkage fields 1256: 1254: 1253: 1248: 1236: 1234: 1233: 1228: 1208: 1206: 1205: 1200: 1198: 1194: 1193: 1192: 1187: 1186: 1161: 1160: 1153: 1152: 1151: 1131: 1129: 1118: 1099: 1098: 1092: 1091: 1039: 1038: 1032: 1018: 1016: 1015: 1010: 998: 996: 995: 992:{\displaystyle } 990: 962: 960: 959: 954: 942: 940: 939: 934: 932: 931: 766:luminance detail 681:or conventional 618:Other approaches 520:video recordings 439:High-Com II 405: 390: 380:'s (Aurex AD-4) 343: 328: 304:dbx Professional 177: 176: 156: 21: 4173: 4172: 4168: 4167: 4166: 4164: 4163: 4162: 4158:Sound recording 4143:Noise reduction 4133: 4132: 4131: 4126: 4070: 4031:Post-processing 4025: 4020: 3990: 3981: 3955:Non-local means 3928: 3909:Low-pass filter 3894: 3888: 3874:Noise generator 3864:Colors of noise 3842: 3749: 3745: 3734: 3730: 3679: 3601: 3595: 3575:Coherent noise 3551:(thermal noise) 3481: 3405: 3391:Noise reduction 3349: 3343: 3286: 3281: 3280: 3263: 3259: 3226: 3222: 3213: 3211: 3207: 3201: 3182: 3174: 3170: 3138:10.1.1.219.5398 3113: 3109: 3070: 3066: 3057: 3055: 3051: 3020: 3014: 3010: 2995: 2991: 2960: 2953: 2944: 2942: 2927: 2901: 2897: 2888: 2886: 2835: 2831: 2780: 2776: 2761: 2727: 2723: 2717: 2713: 2704: 2702: 2687: 2661: 2657: 2620: 2616: 2607: 2605: 2590: 2564: 2560: 2535: 2531: 2524: 2506: 2502: 2495: 2491: 2477: 2475: 2464: 2460: 2451: 2449: 2440: 2439: 2435: 2426: 2424: 2415: 2414: 2410: 2401: 2399: 2390: 2389: 2385: 2376: 2374: 2365: 2364: 2360: 2351: 2349: 2340: 2339: 2335: 2326: 2324: 2315: 2314: 2310: 2293: 2289: 2280: 2278: 2274: 2263: 2257: 2256: 2252: 2243: 2241: 2232: 2231: 2227: 2214: 2212: 2192: 2178: 2174: 2165:. p. 731: 2161: 2159: 2155: 2136: 2125: 2119: 2102: 2093: 2091: 2080:"MXR Compander" 2076: 2072: 2057: 2053: 2044: 2042: 2038: 2025: 2019: 2015: 2000: 1996: 1981: 1977: 1967: 1965: 1961: 1954: 1948: 1947: 1943: 1933: 1931: 1927: 1913: 1909: 1908: 1883: 1869: 1861: 1859: 1844: 1810: 1806: 1775: 1771: 1734: 1730: 1699: 1695: 1672: 1668: 1631: 1627: 1590: 1586: 1555: 1551: 1514: 1510: 1471: 1467: 1462: 1450: 1442:Video denoising 1423: 1377: 1369:Signal subspace 1360: 1355: 1343: 1319: 1292: 1286: 1269: 1263: 1242: 1239: 1238: 1216: 1213: 1212: 1188: 1182: 1181: 1180: 1156: 1155: 1147: 1143: 1136: 1122: 1117: 1113: 1109: 1094: 1093: 1087: 1083: 1034: 1033: 1028: 1026: 1023: 1022: 1004: 1001: 1000: 972: 969: 968: 948: 945: 944: 927: 923: 921: 918: 917: 903: 890: 856: 843: 841:Non-local means 837: 835:Non-local means 815:similar to the 809: 794:Gaussian filter 786:low-pass filter 778: 762: 743: 738: 695: 687:computer vision 679:digital cameras 675: 659: 620: 588:recordings and 528: 399: 384: 364:, Telefunken's 337: 322: 284: 207: 206: 198: 196: 195: 194: 193: 178: 170: 167: 160: 154: 149: 141:seismic imaging 137: 129: 107:In the case of 60:Noise rejection 44:Noise reduction 39: 28: 23: 22: 15: 12: 11: 5: 4171: 4161: 4160: 4155: 4150: 4145: 4128: 4127: 4125: 4124: 4119: 4114: 4109: 4104: 4099: 4094: 4089: 4078: 4076: 4072: 4071: 4069: 4068: 4063: 4058: 4053: 4052: 4051: 4041: 4035: 4033: 4027: 4026: 4019: 4018: 4011: 4004: 3996: 3987: 3986: 3983: 3982: 3980: 3979: 3974: 3968: 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3279: 3278: 3257: 3220: 3199: 3168: 3107: 3080:(1): 137–146. 3064: 3031:(3): 259–302. 3008: 3003:Academic Press 2989: 2970:(4): 653–664. 2951: 2925: 2895: 2849:(2): 149–159. 2829: 2794:(6): 608–616. 2774: 2759: 2721: 2711: 2685: 2655: 2614: 2588: 2558: 2547:(4): 519–538. 2529: 2522: 2500: 2489: 2458: 2433: 2408: 2383: 2358: 2333: 2308: 2287: 2268:AEG-Telefunken 2250: 2225: 2172: 2100: 2070: 2067:(11): 906–911. 2051: 2013: 1994: 1975: 1941: 1881: 1804: 1769: 1748:(1): 457–469. 1728: 1693: 1676:Interpretation 1666: 1645:(2): 768–779. 1625: 1584: 1549: 1528:(1): S11–S20. 1508: 1481:(6): WD1–WD9. 1464: 1463: 1461: 1458: 1457: 1456: 1449: 1446: 1445: 1444: 1439: 1434: 1429: 1422: 1419: 1418: 1417: 1412: 1407: 1402: 1397: 1392: 1387: 1376: 1373: 1372: 1371: 1366: 1359: 1356: 1354: 1351: 1342: 1339: 1318: 1315: 1288:Main article: 1285: 1282: 1265:Main article: 1262: 1259: 1246: 1226: 1223: 1220: 1197: 1191: 1185: 1179: 1176: 1173: 1170: 1167: 1164: 1159: 1150: 1146: 1142: 1139: 1135: 1128: 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1361: 1350: 1348: 1338: 1336: 1332: 1328: 1324: 1323:deep learning 1317:Deep learning 1314: 1312: 1308: 1304: 1300: 1296: 1291: 1281: 1278: 1274: 1268: 1258: 1244: 1237:and variance 1224: 1221: 1218: 1209: 1195: 1189: 1174: 1168: 1165: 1162: 1148: 1144: 1140: 1137: 1133: 1126: 1123: 1119: 1114: 1110: 1106: 1103: 1100: 1088: 1084: 1080: 1077: 1067: 1061: 1058: 1055: 1052: 1046: 1040: 1020: 1006: 983: 980: 977: 966: 950: 928: 924: 914: 912: 908: 898: 894: 885: 882: 875: 871: 868: 865: 864: 863: 861: 860:median filter 851: 848: 842: 832: 830: 826: 825:heat equation 822: 818: 817:heat equation 814: 804: 802: 797: 795: 791: 787: 783: 773: 771: 770:chroma detail 767: 754: 751: 748: 747: 746: 733: 731: 726: 724: 720: 716: 711: 709: 704: 700: 690: 688: 684: 680: 670: 668: 664: 654: 652: 648: 644: 643:noise blanker 639: 637: 633: 629: 625: 615: 613: 609: 605: 602: 599: 594: 591: 587: 586:magnetic tape 582: 578: 573: 571: 567: 563: 559: 555: 550: 548: 544: 540: 536: 532: 523: 521: 518:Since analog 516: 513: 510:, founder of 509: 505: 501: 499: 498:D NR Expander 495: 491: 490: 485: 482: 477: 475: 470: 467: 462: 460: 456: 452: 448: 444: 440: 436: 435:dbx disc 431: 429: 425: 421: 417: 413: 409: 403: 398: 394: 388: 383: 379: 375: 371: 367: 363: 359: 355: 351: 347: 341: 336: 332: 326: 321: 317: 313: 309: 305: 301: 297: 293: 289: 279: 277: 272: 270: 266: 262: 258: 254: 250: 246: 242: 238: 234: 230: 226: 225:surface noise 221: 219: 215: 211: 205: 203: 191: 187: 183: 166: 144: 142: 132: 124: 122: 118: 114: 113:magnetic tape 110: 105: 103: 99: 95: 90: 88: 84: 80: 76: 72: 67: 65: 61: 57: 53: 49: 45: 41: 37: 36:Noise control 33: 32:Soundproofing 19: 4112:Uncompressed 3836: 3826: 3816: 3801: 3797: 3793: 3783: 3779: 3769: 3759: 3740: 3725: 3718: 3712: 3708: 3698: 3694: 3639:Noise figure 3600:Engineering 3589:Worley noise 3519:Cosmic noise 3442:Human health 3390: 3272:1711.10925v2 3260: 3251: 3237: 3233: 3223: 3212:. 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Index

Pumping (noise reduction)
Soundproofing
Noise control
noise
signal
distort
common-mode rejection ratio
signal processing
analog
digital
white noise
algorithms
electronic systems
noise
photographic film
magnetic tape
ferric oxide
magnetite
seismic imaging
Noise reduction example
Audacity
dB
Hz
media help
Tape hiss
analog tape recording
tape heads
surface noise
Dolby HX Pro
DNL

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