1、Comment g1: 隔音Comment g2: 科学的Comment g3: 性状Comment g4: 敏感Noise Reduction(http:/en.wikipedia.org/wiki/Noise_reduction)From Wikipedia, the free encyclopediaFor sound proofing, see soundproofing. For scientific aspects of noise reduction of machinery and products, see noise control.This article needs a
2、dditional citations for verification. Please help improve this article by adding citations to reliable sources. Unsourced material may be challenged and removed. (March 2010)Noise reduction is the process of removing noise from a signal.All recording devices, both analogue or digital, have traits wh
3、ich make them susceptible to noise. Noise can be random or white noise with no coherence, or coherent noise introduced by the devices mechanism or processing algorithms.In electronic recording devices, a major form of noise is hiss caused by random electrons that, heavily influenced by heat, stray f
4、rom their designated path. These stray electrons influence the voltage of the output signal and thus create detectable noise.In the case of photographic film and magnetic tape, noise (both visible and audible) is introduced due to the grain structure of the medium. In photographic film, the size of
5、the grains in the film determines the films sensitivity, more sensitive film having larger sized grains. In magnetic tape, the larger the grains of the magnetic particles (usually ferric oxide or magnetite), the more prone the medium is to noise.To compensate for this, larger areas of film or magnet
6、ic tape may be used to lower the noise to an acceptable level.Contentshide 1 In audioo 1.1 Dolby and dbx noise reduction systemo 1.2 Dynamic Noise Reductiono 1.3 Other approaches 2 In imageso 2.1 Typeso 2.2 Removal 2.2.1 Tradeoffs 2.2.2 Chroma and luminance noise separation 2.2.3 Linear smoothing fi
7、lters 2.2.4 Signal combination 2.2.5 Anisotropic diffusion 2.2.6 Non-local means 2.2.7 Nonlinear filterso 2.3 Software programs 3 See alsoo 3.1 General noise issueso 3.2 Audioo 3.3 Video 4 References 5 External linkseditIn audioNoise reduction exampleMoreExample of noise reduction using Audacity wit
8、h 0 dB, 5 dB, 12 dB, and 30 dB reduction, 150 Hz frequency smoothing, and 0.15 seconds attack/decay time.Problems listening to this file? See media help.When using analog tape recording technology, they may exhibit a type of noise known as tape hiss. This is related to the particle size and texture
9、used in the magnetic emulsion that is sprayed on the recording media, and also to the relative tape velocity across the tape heads.Four types of noise reduction exist: single-ended pre-recording, single-ended hiss reduction, single-ended surface noise reduction, and codec or dual-ended systems. Sing
10、le-ended pre-recording systems (such as Dolby HX Pro) work to affect the recording medium at the time of recording. Single-ended hiss reduction systems (such as DNR) work to reduce noise as it occurs, including both before and after the recording process as well as for live broadcast applications. S
11、ingle-ended surface noise reduction (such as CEDAR and the earlier SAE 5000A and Burwen TNE 7000) is applied to the playback of phonograph records to attenuate the sound of scratches, pops, and surface non-linearities. Dual-ended systems (such as Dolby NR and dbx Type I and II) have a pre-emphasis p
12、rocess applied during recording and then a de-emphasis process applied at playback.editDolby and dbx noise reduction systemWhile there are dozens of different kinds of noise reduction, the first widely used audio noise reduction technique was developed by Ray Dolby in 1966. Intended for professional
13、 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). The Dolby B system (developed in conjunction with Henry Kloss) was a single band system designed fo
14、r consumer products. 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 10dB depending on the initial signal volume. When it was played back, the decoder reversed the pr
15、ocess, in effect reducing the noise level by up to 10dB. The Dolby B system, while not as effective as Dolby A, had the advantage of remaining listenable on playback systems without a decoder.Dbx was the competing analog noise reduction system developed by dbx laboratories. It used a root-mean-squar
16、ed (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 as an open ended system. However it could achieve up to 30 dB of noise reduction. Since
17、Analog video recordings use frequency modulation for the luminance part (composite video signal in direct colour systems), which keeps the tape at saturation level, audio style noise reduction is unnecessary.editDynamic Noise ReductionDynamic Noise Reduction (DNR) is an audio noise reduction system,
18、 introduced by National Semiconductor to reduce noise levels on long-distance telephony.1 First sold in 1981, DNR is frequently confused with the far more common Dolby noise reduction system.2 However, unlike Dolby and dbx Type I the defining characteristic is that the value of a noisy pixel bears n
19、o relation to the color of surrounding pixels. Generally this type of noise will only affect a small number of image pixels. When viewed, the image contains dark and white dots, hence the term salt and pepper noise. Typical sources include flecks of dust inside the camera and overheated or faulty CC
20、D elements.In Gaussian noise, 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 a normal distribution of noise. While other distributions are
21、possible, the Gaussian (normal) distribution is usually a good model, due to the central limit theorem that 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 val
22、ues at different pixels are modeled as being independent and identically distributed, and hence uncorrelated.editRemovaleditTradeoffsIn selecting a noise reduction algorithm, one must weigh several factors: the available computer power and time available: a digital camera must apply noise reduction
23、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
24、noise and the detail in the image, to better make those decisionseditChroma and luminance noise separationIn real-world photographs, the highest spatial-frequency detail consists mostly of variations in brightness (“luminance detail“) rather than variations in hue (“chroma detail“). Since any noise
25、reduction algorithm should attempt to remove noise without sacrificing real detail from the scene photographed, one risks a greater loss of detail from luminance noise reduction than chroma noise reduction simply because most scenes have little high frequency chroma detail to begin with. In addition
26、, most people find chroma noise in images more objectionable than luminance noise; the colored blobs are considered “digital-looking“ and unnatural, compared to the grainy appearance of luminance noise that some compare to film grain. For these two reasons, most photographic noise reduction algorith
27、ms split the image detail into chroma and luminance components and apply more noise reduction to the former.Most dedicated noise-reduction computer software allows the user to control chroma and luminance noise reduction separately.editLinear smoothing filtersOne method to remove noise is by convolv
28、ing the original image with a mask that represents a low-pass filter or 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 smoothi
29、ng filter sets each pixel to the average value, or a weighted average, of itself and its nearby neighbors; the Gaussian filter is 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 neigh
30、borhood would “smear“ 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.editSignal combinationTwo measurements of the same physical quantity often exhibit differe
31、nt noise levels in different frequency ranges. Therefore, a single high-fidelity signal can be constructed by combining the low-noise parts of the signals in Fourier space. The strength of noise reduction by signal combination is that we do not see the loss of information that occurs in other noise-
32、suppression approaches such as filtering or smoothing.3 Noise reduction by signal combination has found applications in in-car microphone systems, single molecule biophysics, chemometrics among other disciplines.editAnisotropic diffusionMain article: Anisotropic diffusionAnother method for removing
33、noise is to evolve the image under a smoothing partial differential equation similar to the heat equation which is called anisotropic diffusion. With a spatially constant diffusion coefficient, this is equivalent to the heat equation or linear Gaussian filtering, but with a diffusion coefficient des
34、igned to detect edges, the noise can be removed without blurring the edges of the image.editNon-local meansMain article: Non-local meansAnother approach for removing noise is based on non-local averaging of all the pixels in an image. In particular, the amount of weighting for a pixel is based on th
35、e degree of similarity between a small patch centered around that pixel and the small patch centered around the pixel being de-noised.editNonlinear filtersA median filter is an example of a non-linear filter and, if properly designed, is very good at preserving image detail. To run a median filter:1
36、. consider each pixel in the image2. sort the neighbouring pixels into order based upon their intensities3. replace the original value of the pixel with the median value from the listA median filter is a rank-selection (RS) filter, a particularly harsh member of the family of rank-conditioned rank-s
37、election (RCRS) filters;4 a much milder member of that family, for example one that selects the closest of the neighboring values when a pixels value is external in its neighborhood, and leaves it unchanged otherwise, is sometimes preferred, especially in photographic applications.Median and other R
38、CRS 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.editSoftware programsMost general purpose image and photo editing software will have one or more noise reduction function
39、s (median, blur, despeckle, etc.). Special purpose noise reduction software programs include Neat Image,Grain Surgery, Noise Ninja, DenoiseMyImage, GREYCstoration (now GMIC), and pnmnlfilt (nonlinear filter) found in the open source Netpbm tools. General purpose image and photo editing software incl
40、uding noise reduction functions include Adobe Photoshop, GIMP, PhotoImpact, Paint Shop Pro, and Helicon Filter.editSee alsoeditGeneral noise issues Digital image processing Noise print Signal (electronics) Signal processing Signal subspaceeditAudio Architectural acoustics Noise-canceling headphones
41、Sound maskingeditVideo Dark frame Video denoisingeditReferences1. B. Boashash, editor, “Time-Frequency Signal Analysis and Processing A Comprehensive Reference“, Elsevier Science, Oxford, 2003; ISBN 0-08-044335-42. B. Boashash, “Estimating and Interpreting the Instantaneous Frequency of a Signal-Par
42、t I: Fundamentals“, Proceedings of the IEEE, Vol. 80, No. 4, pp. 519-538, April 1992, doi:10.1109/5.1353763. Mashaghi et al. Noise reduction by signal combination in Fourier space applied to drift correction in optical tweezers, Rev. Sci. Instrum. 82, 115103 (2011)4. Puyin Liu and Hongxing Li (2004)
43、. Fuzzy Neural Network Theory and Application. World Scientific. ISBN 981-238-786-2.editExternal links Noise reduction for Pixel Sensors Recent trends in denoising tutorial Noise Reduction in photography Matlab software and Photoshop plug-in for image denoising (Pointwise SA-DCT filter) Matlab softw
44、are for image and video denoising (Non-local transform-domain filter) Non-local image denoising, with code and online demonstrationhide V T ENoise (in physics and telecommunications)General Distortion Noise control Noise measurement Noise power Noise reduction Noise temperature Phase distortionNoise
45、 in. Audio Electronics Images Radio VideoClass of noise Additive white Gaussian noise (AWGN) Atmospheric noise Background noise Brownian noise Burst noise Cosmic noise Flicker noise Gaussian noise Grey noise Jitter JohnsonNyquist noise Pink noise Quantization error (or q. noise) Shot noise White noi
46、seEngineering terms Channel noise level Circuit noise level Effective input noise temperature Equivalent noise resistance Equivalent pulse code modulation noise Impulse noise (audio) Noise figure Noise floor Noise shaping Noise spectral density Phase noise Pseudorandom noise Statistical noiseRatios
47、Carrier-to-noise ratio (C/N) Carrier-to-receiver noise density (C/kT) dBrnC Eb/N0 (energy per bit to noise density) Es/N0 (energy per symbol to noise density) Modulation error ratio (MER) Signal, noise and distortion (SINAD) Signal-to-interference ratio (S/I) Signal-to-noise ratio (S/N, SNR) Signal to noise ratio (imaging) Signal-to-noise plus interference (SNIR) Signal-to-quantization-noise ratio (SQNR)Related topics List of noise topics Colors of noise Interference (communication) Radio noise source Spectrum analyzer Thermal radiationshow V T EVideo processing
