Wikipedia

Noise reduction

(Redirected fromDigital noise reduction)
For the reduction of a sound's volume, seeSoundproofing.For the noise reduction of machinery and products, seeNoise control.

Noise reductionis the process of removingnoisefrom asignal.Noise reduction techniques exist for audio and images. Noise reduction algorithms maydistortthe signal to some degree.Noise rejectionis the ability of a circuit to isolate an undesired signal component from the desired signal component, as withcommon-mode rejection ratio.

Allsignal processingdevices, bothanaloganddigital,have traits that make them susceptible to noise. Noise can be random with an even frequency distribution (white noise), or frequency-dependent noise introduced by a device's mechanism or signal processingalgorithms.

Inelectronic systems,a major type of noise ishisscreated by random electron motion due to thermal agitation. These agitated electrons rapidly add and subtract from the output signal and thus create detectablenoise.

In the case ofphotographic filmandmagnetic tape,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 (usuallyferric oxideormagnetite), the more prone the medium is to noise. To compensate for this, larger areas of film or magnetic tape may be used to lower the noise to an acceptable level.

In general

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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.[1]

Noise reduction techniques in Digital Signal Processing (DSP) are essential for improving the quality of signals in various applications, including audio processing, telecommunications, and biomedical engineering. Noise, which is unwanted random variation in signals, can degrade signal clarity and accuracy. DSP offers a range of algorithms to reduce noise while preserving the integrity of the original signal.

Spectral subtraction

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Spectral subtractionis one of the simplest and most widely used noise reduction techniques, especially in speech processing. It works by estimating the power spectrum of the noise during silent periods and subtracting this noise spectrum from the noisy signal. This technique assumes that noise is additive and relatively stationary. While effective, spectral subtraction can introduce "musical noise," a type of artificial noise, if the noise spectrum estimate is inaccurate.

Applications: Primarily used in audio signal processing, including mobile telephony and hearing aids.

Advantages: Simple to implement and computationally efficient.

Limitations: Tends to perform poorly in the presence of non-stationary noise, and can introduce artifacts.

Adaptive filtering

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Adaptive filtersare highly effective in situations where noise is unpredictable or non-stationary. In adaptive filtering, the filter's parameters are continuously adjusted to minimize the difference between the desired signal and the actual output. The Least Mean Squares (LMS) and Recursive Least Squares (RLS) algorithms are commonly used for adaptive noise cancellation.

Applications: Used in active noise-canceling headphones, biomedical devices (e.g., EEG and ECG processing), and communications.

Advantages: Can adapt to changing noise environments in real-time.

Limitations: Higher computational requirements, which may be challenging for real-time applications on low-power devices.

Wiener filtering

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Wiener filteringis a statistical approach to noise reduction that minimizes the mean square error between the desired signal and the actual output. This technique relies on knowledge of both the signal and noise power spectra, and it can provide optimal noise reduction if these spectra are accurately estimated.

Applications: Frequently applied in image processing, audio restoration, and radar.

Advantages: Provides optimal noise reduction for stationary noise.

Limitations: Requires accurate estimates of the signal and noise statistics, which may not always be feasible in real-world applications.

Kalman filtering

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Kalman filteringis a recursive algorithm that estimates the state of a dynamic system from a series of noisy measurements. While typically used for tracking and prediction, it is also applicable to noise reduction, especially for signals that can be modeled as time-varying. Kalman filtering is particularly effective in applications where the signal is dynamic and the noise characteristics vary over time.

Applications: Used in speech enhancement, radar, and control systems.

Advantages: Provides excellent performance for time-varying signals with non-stationary noise.

Limitations: Requires a mathematical model of the system dynamics, which may be complex to design for certain applications.

Wavelet-based denoising

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Wavelet-based denoising (or wavelet thresholding) decomposes the signal into different frequency components using a wavelet transform and then removes the noise by thresholding the wavelet coefficients. This method is effective for signals with sharp transients, like biomedical signals, because wavelet transforms can provide both time and frequency information.

Applications: Commonly used in image processing, ECG and EEG signal denoising, and audio processing.

Advantages: Preserves sharp signal features and offers flexibility in handling non-stationary noise.

Limitations: The choice of wavelet basis and thresholding parameters significantly impacts performance, requiring careful tuning.

Non-local means denoising

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Non-local means(NLM) is an advanced noise reduction technique that uses redundancy in the signal by averaging similar patches across the signal or image. While computationally more demanding, NLM is highly effective in removing noise from images and audio signals without blurring.

Applications: Applied primarily in image denoising, especially in medical imaging and photography.

Advantages: Preserves details and edges in images.

Limitations: Computationally intensive, often requiring hardware acceleration or approximations for real-time applications.

In seismic exploration

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Boosting signals in seismic data is especially crucial forseismic imaging,[2][3]inversion,[4][5]and interpretation,[6]thereby greatly improving the success rate in oil & gas exploration.[7][8][9]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.

In audio

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Noise reduction example
Example of noise reduction usingAudacitywith 0dB,5 dB, 12 dB, and 30 dB reduction, 150Hzfrequency smoothing, and 0.15 seconds attack/decay time.
Problems playing this file? Seemedia help.

Tape hissis a performance-limiting issue inanalog tape recording.This is related to the particle size and texture used in the magnetic emulsion that is sprayed on the recording media, and also to the relative tape velocity across thetape heads.

Four types of noise reduction exist: single-ended pre-recording, single-ended hiss reduction, single-endedsurface noisereduction, and codec or dual-ended systems. Single-ended pre-recording systems (such asDolby HX Pro), work to affect the recording medium at the time of recording. Single-ended hiss reduction systems (such asDNL[10]orDNR) work to reduce noise as it occurs, including both before and after the recording process as well as for live broadcast applications. Single-ended surface noise reduction (such asCEDARand the earlier SAE 5000A,BurwenTNE 7000, andPackburn101/323/323A/323AA and 325[11]) is applied to the playback ofphonograph recordsto address scratches, pops, and surface non-linearities. Single-endeddynamic range expanderslike thePhase LinearAutocorrelator Noise Reduction and Dynamic Range Recovery System (Models 1000 and 4000) can reduce various noise from old recordings. Dual-ended systems (such asDolby noise-reduction systemordbx) have a pre-emphasis process applied during recording and then a de-emphasis process applied during playback.

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 thatdithersystems actually add noise to a signal to improve its quality.

Compander-based noise reduction systems

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Dual-endedcompandernoise reduction systems have a pre-emphasis process applied during recording and then a de-emphasis process applied at playback. Systems include the professional systemsDolby A[10]andDolby SRbyDolby Laboratories,dbx Professionalanddbx Type Ibydbx,Donald Aldous'EMTNoiseBX,[12]Burwen Noise Eliminator[it],[13][14][15]Telefunken'stelcom c4[de][10]and MXR Innovations' MXR[16]as well as the consumer systemsDolby NR,Dolby B,[10]Dolby CandDolby S,dbx Type II,[10]Telefunken'sHigh Com[10]andNakamichi'sHigh-Com II,Toshiba's (Aurex AD-4)adres[ja],[10][17]JVC'sANRS[ja][10][17]andSuper ANRS,[10][17]Fisher/Sanyo'sSuper D,[18][10][17]SNRS,[17]and the Hungarian/East-GermanEx-Kosystem.[19][17]

In some compander systems, the compression is applied during professional media production and only the expansion is applied by the listener; for example, systems likedbx disc,High-Com II,CX 20[17]andUCused for vinyl recordings andDolby FM,High Com FMandFMXused in FM radio broadcasting.

The first widely used audio noise reduction technique was developed byRay Dolbyin 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 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.

The Dolby B system (developed in conjunction withHenry Kloss) was a single-band system designed for consumer products. The Dolby B system, while not as effective as Dolby A, had the advantage of remaining listenable on playback systems without a decoder.

TheTelefunkenHigh Comintegrated circuitU401BRcould be utilized to work as a mostlyDolby B–compatible compander as well.[20]In various late-generation High Com tape decks the Dolby-B emulatingD NR Expanderfunctionality worked not only for playback, but, as an undocumented feature, also during recording.

dbxwas a competing analog noise reduction system developed byDavid E. Blackmer,founder ofDbx, Inc.[21]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 of noise reduction.

Since analogvideo recordingsuse 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.

Dynamic noise limiter and dynamic noise reduction

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Dynamic noise limiter(DNL) is an audio noise reduction system originally introduced byPhilipsin 1971 for use oncassette decks.[10]Its circuitry is also based on a singlechip.[22][23]

It was further developed intodynamic noise reduction(DNR) byNational Semiconductorto reduce noise levels on long-distancetelephony.[24]First sold in 1981, DNR is frequently confused with the far more commonDolby noise-reduction system.[25]

Unlike Dolby anddbx Type IandType IInoise 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, includingmagnetic taperecordings andFM radiobroadcasts, reducing noise by as much as 10 dB.[26]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.[27]

One of DNR's first widespread applications was in theGMDelcocar stereosystems in US GM cars introduced in 1984.[28]It was also used in factory car stereos inJeepvehicles in the 1980s, such as theCherokee XJ.Today, DNR, DNL, and similar systems are most commonly encountered as a noise reduction system in microphone systems.[29]

Other approaches

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A second class of algorithms work in the time-frequency domain using somelinearornonlinear filtersthat have local characteristics and are often calledtime-frequency filters.[30][page needed]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 orwanted noise,will be untouched. The region is typically defined by the location of the signal's instantaneous frequency,[31]as most of the signal energy to be preserved is concentrated about it.

Yet another approach is the automatic noise limiter andnoise blankercommonly found onHAM radiotransceivers,CB radiotransceivers, 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.

Software programs

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Mostdigital audio workstations(DAWs) andaudio editing softwarehave one or more noise reduction functions.

In images

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Images taken withdigital camerasor conventionalfilm cameraswill 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 ascomputer vision.

Types

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Insalt and pepper noise(sparse light and dark disturbances),[32]also known as impulse noise,[33]pixelsin 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. 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 faultyCCDelements.

InGaussian noise,[34]each pixel in the image will be changed from its original value by a (usually) small amount. A histogram, a plot of the amount of distortion of a pixel value against the frequency with which it occurs, shows anormal distributionof noise. While other distributions are possible, the Gaussian (normal) distribution is usually a good model, due to thecentral limit theoremthat says that the sum of different noises tends to approach a Gaussian distribution.

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 beingindependent and identically distributed,and hence uncorrelated.

Removal

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Tradeoffs

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There are many noise reduction algorithms in image processing.[35]In selecting a noise reduction algorithm, one must weigh several factors:

  • 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
  • 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)
  • the characteristics of the noise and the detail in the image, to better make those decisions

Chroma and luminance noise separation

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In real-world photographs, the highest spatial-frequency detail consists mostly of variations in brightness (luminance detail) rather than variations in hue (chroma detail). 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.

Linear smoothing filters

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One method to remove noise is byconvolvingthe original image with a mask that represents alow-pass filteror smoothing operation. For example, the Gaussian mask comprises elements determined by aGaussian function.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; theGaussian filteris just one possible set of weights.

Smoothing filters tend to blur an image because pixel intensity values that are significantly higher or lower than the surrounding neighborhoodsmearacross the area. Because of this blurring, linear filters are seldom used in practice for noise reduction;[citation needed]they are, however, often used as the basis for nonlinear noise reduction filters.

Anisotropic diffusion

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Another method for removing noise is to evolve the image under a smoothingpartial differential equationsimilar to theheat equation,which is calledanisotropic diffusion.With a spatially constant diffusion coefficient, this is equivalent to theheat equationor linearGaussian filtering,but with a diffusion coefficient designed to detect edges, the noise can be removed without blurring the edges of the image.

Non-local means

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Main article:Non-local means

Another approach for removing noise is based onnon-localaveraging 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.

Nonlinear filters

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Amedian filteris an example of a nonlinear filter and, if properly designed, is very good at preserving image detail. To run a median filter:

  1. consider each pixel in the image
  2. sort the neighbouring pixels into order based upon their intensities
  3. replace the original value of the pixel with themedianvalue from the list

A median filter is a rank-selection (RS) filter, a particularly harsh member of the family of rank-conditioned rank-selection (RCRS) filters;[36]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, and leaves it unchanged otherwise, is sometimes preferred, especially in photographic applications.

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.

Wavelet transform

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The main aim of an image denoising algorithm is to achieve both noise reduction[37]and feature preservation[38]using the wavelet filter banks.[39]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 image information is concentrated in a few large ones.[40]Therefore, the first wavelet-based denoising methods were based on thresholding of detail subband coefficients.[41][page needed]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.

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 signal and noise components.[40]

Statistical methods

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Statistical methods for image denoising exist as well. ForGaussian noise,one can model the pixels in a greyscale image as auto-normally distributed, where each pixel'struegreyscale value is normally distributed with mean equal to the average greyscale value of its neighboring pixels and a given variance.

Letdenote the pixels adjacent to theth pixel. Then theconditional distributionof the greyscale intensity (on ascale) at theth node is:

for a chosen parameterand variance.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.[42][43]

Block-matching algorithms

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Main article:Block-matching and 3D filtering

Ablock-matching algorithmcan be applied to group similar image fragments of overlappingmacroblocksof 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.[44]

Random field

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Main article:Shrinkage Fields (image restoration)

Shrinkage fieldsis arandom field-basedmachine learningtechnique that brings performance comparable to that ofBlock-matching and 3D filteringyet requires much lower computational overhead such that it can be performed directly withinembedded systems.[45]

Deep learning

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Variousdeep learningapproaches have been proposed to achieve noise reduction[46]and suchimage restorationtasks.Deep Image Prioris one such technique that makes use ofconvolutional neural networkand is notable in that it requires no prior training data.[47]

Software

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Most general-purpose image and photo editing software will have one or more noise-reduction functions (median,blur,despeckle, etc.).

See also

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General noise issues

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Audio

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Images and video

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Similar problems

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References

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