Home  ›  Software to Read Image Color on Frequencies

Software to Read Image Color on Frequencies

Written By Tuesday, March 1, 2022 Add Comment Edit

Process of removing noise from a indicate

For the reduction of a sound's volume, come across soundproofing. For the dissonance reduction of machinery and products, meet dissonance control.

Dissonance reduction is the process of removing racket from a signal. Noise reduction techniques exist for audio and images. Noise reduction algorithms may distort the signal to some caste.

All signal processing devices, both analog and digital, have traits that make them susceptible to noise. Noise can be random with an even frequency distribution (white racket), or frequency-dependent racket introduced past a device's mechanism or signal processing algorithms.

In electronic recording devices, a major type of noise is hiss created past random electron motion due to thermal agitation that occurs due to temperature. These agitated electrons rapidly add and decrease from the voltage of the output signal and thus create detectable noise.

In the case of photographic moving-picture show and magnetic tape, noise (both visible and audible) is introduced due to the grain structure of the medium. In photographic moving-picture show, the size of the grains in the flick determines the film's sensitivity, more sensitive motion picture 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 magnetic tape may be used to lower the noise to an adequate level.

In general [edit]

Noise reduction algorithms tend to alter signals to a greater or bottom degree. The local signal-and-noise orthogonalization algorithm can be used to avert changes to the signals.[ane]

In seismic exploration [edit]

Boosting signals in seismic data is especially crucial for seismic imaging,[two] [three] inversion,[4] [five] and estimation,[6] thereby greatly improving the success rate in oil & gas exploration.[7] [8] [9] [10] The useful bespeak that is smeared in the ambient random racket is oftentimes neglected and thus may cause fake discontinuity of seismic events and artifacts in the final migrated prototype. Enhancing the useful signal while preserving edge properties of the seismic profiles by attenuating random noise tin can aid reduce interpretation difficulties and misleading risks for oil and gas detection.

In audio [edit]

When using analog tape recording technology, they may exhibit a blazon of noise known as tape hiss. This is related to the particle size and texture used in the magnetic emulsion that is sprayed on the recording media, and likewise to the relative tape velocity across the tape heads.

Four types of noise reduction exist: single-ended pre-recording, single-ended hiss reduction, single-concluded surface noise reduction, and codec or dual-ended systems. Single-ended pre-recording systems (such every bit Dolby HX and HX Pro, or Tandberg'due south Actilinear and Dyneq[11] [12] [13] [14]) work to impact the recording medium at the fourth dimension of recording. Unmarried-concluded hiss reduction systems (such as DNL[fifteen] or DNR) piece of work to reduce noise every bit information technology occurs, including both before and after the recording process too as for alive broadcast applications. Single-concluded surface noise reduction (such every bit CEDAR and the earlier SAE 5000A, Burwen TNE 7000, and Packburn 101/323/323A/323AA and 325[xvi]) is applied to the playback of phonograph records to attenuate the audio of scratches, pops, and surface not-linearities. Single-concluded dynamic range expanders like the Phase Linear Autocorrelator Noise Reduction and Dynamic Range Recovery Organisation (Models one thousand and 4000) can reduce various noise from former recordings. Dual-ended systems have a pre-emphasis process applied during recording and so a de-emphasis process practical at playback.

Compander-based noise reduction systems [edit]

Dual-ended compander racket reduction systems include the professional systems Dolby A[15] and Dolby SR past Dolby Laboratories, dbx Professional and dbx Type I by dbx, Donald Aldous' EMT NoiseBX,[17] Burwen Laboratories' Model 2000 [it],[18] [nineteen] [20] Telefunken's telcom c4 [de] [xv] and MXR Innovations' MXR[21] too as the consumer systems Dolby NR, Dolby B,[fifteen] Dolby C and Dolby S, dbx Blazon II,[15] Telefunken's High Com[15] and Nakamichi'due south High-Com Ii, Toshiba'southward (Aurex Advertizement-four) adres [ja],[15] [22] JVC'due south ANRS [ja] [15] [22] and Super ANRS,[15] [22] Fisher/Sanyo's Super D,[23] [15] [22] SNRS,[22] and the Hungarian/East-German Ex-Ko organization.[24] [22] These systems take a pre-emphasis procedure applied during recording and then a de-emphasis process applied at playback.

In some compander systems the compression is applied during professional media product and simply the expansion is applied by the listener; for example, systems like dbx disc, High-Com Two, CX 20[22] and UC were used for vinyl recordings whereas Dolby FM, High Com FM and FMX were used in FM radio broadcasting.

The offset widely used sound noise reduction technique was developed past Ray Dolby in 1966. Intended for professional utilise, Dolby Type A was an encode/decode organization in which the aamplitude 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 arrangement designed for consumer products. In particular, when recording quiet parts of an audio betoken, the frequencies higher up 1 kHz would be boosted. This had the outcome of increasing the signal to noise ratio on tape up to 10 dB depending on the initial bespeak volume. When information technology was played back, the decoder reversed the process, in outcome reducing the noise level by upward to 10 dB. The Dolby B system, while not as effective as Dolby A, had the advantage of remaining listenable on playback systems without a decoder.

The Telefunken High Com integrated circuit U401BR could exist utilized to piece of work as a generally Dolby B–compatible compander as well.[25] In various belatedly-generation High Com tape decks the Dolby-B emulating "D NR Expander" functionality worked not merely for playback, but undocumentedly also during recording.

dbx was a competing analog noise reduction arrangement developed past David E. Blackmer, founder of dbx laboratories.[26] Information technology used a root-mean-squared (RMS) encode/decode algorithm with the dissonance-prone loftier frequencies boosted, and the unabridged signal fed through a 2:i compander. dbx operated across the entire audible bandwidth and unlike Dolby B was unusable every bit an open up ended organization. Yet it could achieve upward to 30 dB of noise reduction.

Since analog video recordings use frequency modulation for the luminance part (composite video signal in directly colour systems), which keeps the tape at saturation level, sound manner dissonance reduction is unnecessary.

Dynamic dissonance limiter and dynamic noise reduction [edit]

Dynamic dissonance limiter (DNL) is an audio noise reduction organisation originally introduced by Philips in 1971 for use on cassette decks.[15] Its circuitry is also based on a single fleck.[27] [28]

Information technology was further developed into dynamic noise reduction (DNR) by National Semiconductor to reduce noise levels on long-distance telephony.[29] First sold in 1981, DNR is ofttimes confused with the far more than common Dolby dissonance-reduction system.[30] However, dissimilar Dolby and dbx Blazon I & Type Two dissonance reduction systems, DNL and DNR are playback-just signal processing systems that do not require the source material to first exist encoded, and they tin be used together with other forms of racket reduction.[31]

Because DNL and DNR are not-complementary, meaning they do not require encoded source material, they can be used to remove background noise from whatsoever audio indicate, including magnetic tape recordings and FM radio broadcasts, reducing noise by as much as x dB.[32] They can 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 organization to mistrack.

One of DNR'southward outset widespread applications was in the GM Delco automobile stereo systems in U.S. GM cars introduced in 1984.[33] Information technology was also used in factory car stereos in Jeep vehicles in the 1980s, such as the Cherokee XJ. Today, DNR, DNL, and similar systems are most commonly encountered as a dissonance reduction system in microphone systems.[34]

Other approaches [edit]

A second form of algorithms work in the time-frequency domain using some linear or non-linear filters that have local characteristics and are often called fourth dimension-frequency filters.[35] [ page needed ] Dissonance can therefore be also removed past utilize of spectral editing tools, which work in this fourth dimension-frequency domain, allowing local modifications without affecting nearby signal free energy. This can exist done manually by using the mouse with a pen that has a defined fourth dimension-frequency shape. This is washed much similar in a paint program cartoon pictures. Some other way is to define a dynamic threshold for filtering noise, that is derived from the local betoken, again with respect to a local time-frequency region. Everything below the threshold volition be filtered, everything above the threshold, like partials of a phonation or "wanted noise", will be untouched. The region is typically defined past the location of the betoken Instantaneous Frequency,[36] as almost of the bespeak energy to be preserved is full-bodied about information technology.

Mod digital sound (and picture) recordings no longer need to worry about record hiss and so analog manner noise reduction systems are not necessary. However, an interesting twist is that dither systems actually add noise to a signal to improve its quality.

Software programs [edit]

Most DAWs (Digital sound workstation) and sound software in full general accept one or more noise reduction functions. Notable special purpose noise reduction software programs include Gnome Wave Cleaner.

In images [edit]

Noise reduction by correlation

Images taken with both digital cameras and conventional film cameras will pick upwardly noise from a multifariousness of sources. Further use of these images will often require that the noise be (partially) removed – for aesthetic purposes as in artistic piece of work or marketing, or for applied purposes such equally computer vision.

Types [edit]

In salt and pepper noise (sparse light and dark disturbances), pixels 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 colour of surrounding pixels. By and large this blazon of noise will only touch on a small number of image pixels. When viewed, the prototype contains night and white dots, hence the term salt and pepper noise. Typical sources include flecks of dust inside the camera and overheated or faulty CCD elements.

In Gaussian noise, each pixel in the image will exist changed from its original value by a (usually) minor amount. A histogram, a plot of the corporeality of distortion of a pixel value against the frequency with which it occurs, shows a normal distribution of noise. While other distributions are possible, the Gaussian (normal) distribution is usually a skillful model, due to the fundamental limit theorem that says that the sum of unlike noises tends to approach a Gaussian distribution.

In either case, the dissonance at different pixels can be either correlated or uncorrelated; in many cases, noise values at unlike pixels are modeled as being contained and identically distributed, and hence uncorrelated.

Removal [edit]

Tradeoffs [edit]

At that place are many dissonance reduction algorithms in image processing.[37] In selecting a racket reduction algorithm, 1 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 fourth dimension
  • whether sacrificing some existent detail is acceptable if information technology allows more noise to be removed (how aggressively to make up one's mind whether variations in the image are noise or not)
  • the characteristics of the racket and the detail in the prototype, to ameliorate brand those decisions

Blush and luminance noise separation [edit]

In real-world photographs, the highest spatial-frequency detail consists mostly of variations in brightness ("luminance detail") rather than variations in hue ("chroma particular"). Since whatever dissonance reduction algorithm should attempt to remove racket without sacrificing existent detail from the scene photographed, 1 risks a greater loss of item from luminance noise reduction than chroma noise reduction simply because most scenes accept little loftier frequency chroma detail to begin with. In addition, most people detect chroma noise in images more objectionable than luminance racket; 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, nigh photographic dissonance reduction algorithms split up the paradigm detail into chroma and luminance components and utilise more noise reduction to the onetime.

Virtually dedicated noise-reduction computer software allows the user to control chroma and luminance noise reduction separately.

Linear smoothing filters [edit]

One method to remove noise is by convolving the original prototype with a mask that represents a low-pass filter or smoothing operation. For example, the Gaussian mask comprises elements adamant by a Gaussian 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; the Gaussian filter is but i possible fix of weights.

Smoothing filters tend to blur an image, because pixel intensity values that are significantly college or lower than the surrounding neighborhood would "smear" across the expanse. Considering of this blurring, linear filters are seldom used in practice for noise reduction; they are, all the same, often used as the basis for nonlinear noise reduction filters.

Anisotropic diffusion [edit]

Another method for removing noise is to evolve the image under a smoothing partial differential equation like 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 designed to discover edges, the noise tin can exist removed without blurring the edges of the image.

Non-local means [edit]

Another arroyo for removing noise is based on non-local averaging of all the pixels in an epitome. In particular, the amount of weighting for a pixel is based on the caste of similarity between a pocket-sized patch centered on that pixel and the small-scale patch centered on the pixel being de-noised.

Nonlinear filters [edit]

A median filter is an example of a non-linear filter and, if properly designed, is very proficient at preserving image detail. To run a median filter:

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

A median filter is a rank-selection (RS) filter, a specially harsh member of the family unit of rank-conditioned rank-choice (RCRS) filters;[38] a much milder member of that family unit, for case ane that selects the closest of the neighboring values when a pixel'due south value is external in its neighborhood, and leaves it unchanged otherwise, is sometimes preferred, especially in photographic applications.

Median and other RCRS filters are practiced at removing salt and pepper noise from an image, and as well crusade relatively piffling blurring of edges, and hence are often used in figurer vision applications.

Wavelet transform [edit]

The main aim of an prototype denoising algorithm is to achieve both noise reduction[39] and feature preservation[40] using the wavelet filter banks.[41] In this context, wavelet-based methods are of particular involvement. In the wavelet domain, the dissonance is uniformly spread throughout coefficients while most of the paradigm information is concentrated in a few large ones.[42] Therefore, the first wavelet-based denoising methods were based on thresholding of detail subbands coefficients.[43] [ page needed ] However, most of the wavelet thresholding methods suffer from the drawback that the chosen threshold may non match the specific distribution of indicate and racket components at dissimilar scales and orientations.

To accost these disadvantages, non-linear estimators based on Bayesian theory accept been developed. In the Bayesian framework, it has been recognized that a successful denoising algorithm can attain both noise reduction and characteristic preservation if it employs an accurate statistical description of the point and noise components.[42]

Statistical methods [edit]

Statistical methods for image denoising exist as well, though they are infrequently used as they are computationally demanding. For Gaussian noise, 1 can model the pixels in a greyscale image every bit auto-commonly distributed, where each pixel'southward "truthful" greyscale value is normally distributed with mean equal to the boilerplate greyscale value of its neighboring pixels and a given variance.

Let δ i {\displaystyle \delta _{i}} denote the pixels adjacent to the i {\displaystyle i} th pixel. So the conditional distribution of the greyscale intensity (on a [ 0 , one ] {\displaystyle [0,1]} scale) at the i {\displaystyle i} th node is:

P ( x ( i ) = c | x ( j ) ∀ j ∈ δ i ) ∝ east − β ii λ ∑ j ∈ δ i ( c − 10 ( j ) ) 2 {\displaystyle \mathbb {P} (ten(i)=c|ten(j)\forall j\in \delta i)\propto e^{-{\frac {\beta }{2\lambda }}\sum _{j\in \delta i}(c-x(j))^{ii}}}

for a called parameter β ≥ 0 {\displaystyle \beta \geq 0} and variance λ {\displaystyle \lambda } . One method of denoising that uses the auto-normal model uses the image data equally a Bayesian prior and the car-normal density every bit a likelihood function, with the resulting posterior distribution offering a mean or mode as a denoised image.[44] [45]

Block-matching algorithms [edit]

A block-matching algorithm tin can be applied to group similar image fragments into overlapping macroblocks 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.[46]

Random field [edit]

Shrinkage fields is a random field-based machine learning technique that brings functioning comparable to that of Cake-matching and 3D filtering yet requires much lower computational overhead (such that it could be performed directly inside embedded systems).[47]

Deep learning [edit]

Various deep learning approaches have been proposed to solve noise reduction and such image restoration tasks. Deep Image Prior is one such technique which makes use of convolutional neural network and is distinct in that information technology requires no prior training data.[48]

Software [edit]

Almost full general purpose image and photo editing software will have 1 or more noise-reduction functions (median, mistiness, despeckle, etc.).

See also [edit]

General noise issues [edit]

  • Filter (signal processing)
  • Betoken processing
  • Signal subspace

Audio [edit]

  • Architectural acoustics
  • Codec listening exam
  • Dissonance-cancelling headphones
  • Noise print
  • Sound masking

Images and video [edit]

  • Dark-frame subtraction
  • Digital image processing
  • Total variation denoising
  • Video denoising

Similar problems [edit]

  • Deblurring

References [edit]

  1. ^ Chen, Yangkang; Fomel, Sergey (November–Dec 2015). "Random noise attenuation using local betoken-and-noise orthogonalization". Geophysics. 80 (six): WD1–WD9. Bibcode:2015Geop...80D...1C. doi:10.1190/GEO2014-0227.i. S2CID 120440599.
  2. ^ Xue, Zhiguang; Chen, Yangkang; Fomel, Sergey; Sun, Junzhe (2016). "Seismic imaging of incomplete data and simultaneous-source information using to the lowest degree-squares reverse time migration with shaping regularization". Geophysics. 81 (1): S11–S20. Bibcode:2016Geop...81S..11X. doi:ten.1190/geo2014-0524.1.
  3. ^ Chen, Yangkang; Yuan, Jiang; Zu, Shaohuan; Qu, Shan; Gan, Shuwei (2015). "Seismic imaging of simultaneous-source information using constrained least-squares reverse time migration". Periodical of Applied Geophysics. 114: 32–35. Bibcode:2015JAG...114...32C. doi:ten.1016/j.jappgeo.2015.01.004.
  4. ^ Chen, Yangkang; Chen, Hanming; Xiang, Kui; Chen, Xiaohong (2017). "Geological structure guided well log interpolation for high-allegiance full waveform inversion". Geophysical Journal International. 209 (ane): 21–31. Bibcode:2016GeoJI.207.1313C. doi:10.1093/gji/ggw343.
  5. ^ Gan, Shuwei; Wang, Shoudong; Chen, Yangkang; Qu, Shan; Zu, Shaohuan (2016). "Velocity analysis of simultaneous-source data using loftier-resolution semblance—coping with the strong noise". Geophysical Journal International. 204 (two): 768–779. Bibcode:2016GeoJI.204..768G. doi:x.1093/gji/ggv484.
  6. ^ Chen, Yangkang (2017). "Probing the subsurface karst features using fourth dimension-frequency decomposition". Interpretation. 4 (iv): T533–T542. doi:10.1190/INT-2016-0030.1.
  7. ^ Huang, Weilin; Wang, Runqiu; Chen, Yangkang; Li, Huijian; Gan, Shuwei (2016). "Damped multichannel atypical spectrum analysis for 3D random noise attenuation". Geophysics. 81 (4): V261–V270. Bibcode:2016Geop...81V.261H. doi:10.1190/geo2015-0264.1.
  8. ^ Chen, Yangkang (2016). "Dip-separated structural filtering using seislet transform and adaptive empirical style decomposition based dip filter". Geophysical Journal International. 206 (1): 457–469. Bibcode:2016GeoJI.206..457C. doi:x.1093/gji/ggw165.
  9. ^ Chen, Yangkang; Ma, Jianwei; Fomel, Sergey (2016). "Double-sparsity lexicon for seismic racket attenuation". Geophysics. 81 (4): V261–V270. Bibcode:2016Geop...81V.193C. doi:10.1190/geo2014-0525.1.
  10. ^ Chen, Yangkang (2017). "Fast lexicon learning for noise attenuation of multidimensional seismic data". Geophysical Journal International. 209 (1): 21–31. Bibcode:2017GeoJI.209...21C. doi:10.1093/gji/ggw492.
  11. ^ "Archived copy" (PDF). www.ant-audio.co.great britain. Archived from the original (PDF) on two July 2020. Retrieved 11 January 2022. {{cite web}}: CS1 maint: archived copy as championship (link)
  12. ^ "Archived copy" (PDF). sportsbil.com. Archived from the original (PDF) on 2 July 2020. Retrieved xi Jan 2022. {{cite web}}: CS1 maint: archived copy as title (link)
  13. ^ Information, Reed Business organization (20 September 1979). "New Scientist".
  14. ^ Fantel, Hans (2 September 1984). "Sound; A Standout Cassette Deck". The New York Times. Archived from the original on 2020-07-02.
  15. ^ a b c d e f g h i j m "High Com - the latest noise reduction system / Racket reduction - silence is gilded" (PDF). elektor (United kingdom of great britain and northern ireland) – upward-to-engagement electronics for lab and leisure. Vol. 1981, no. 70. Feb 1981. pp. 2-04–ii-09. Archived (PDF) from the original on 2020-07-02. Retrieved 2020-07-02 . (6 pages)
  16. ^ Audio Dissonance Suppressor Model 325 Owner'southward Transmission (PDF). Rev. xv-1. Syracuse, New York, USA: Packburn electronics inc. Archived (PDF) from the original on 2021-05-05. Retrieved 2021-05-xvi . (6+36 pages)
  17. ^ R., C. (1965). "Kompander verbessert Magnettonkopie". Radio Mentor (in German). 1965 (4): 301–303.
  18. ^ Burwen, Richard S. (Feb 1971). "A Dynamic Noise Filter". Journal of the Sound Engineering science Society. nineteen (1).
  19. ^ Burwen, Richard S. (June 1971). "110 dB Dynamic Range For Tape" (PDF). Sound: 49–l. Archived (PDF) from the original on 2017-11-13. Retrieved 2017-eleven-thirteen .
  20. ^ Burwen, Richard S. (Dec 1971). "Design of a Noise Eliminator System". Journal of the Audio Engineering Society. 19 (xi): 906–911.
  21. ^ Lambert, Mel (September 1978). "MXR Compander". Sound International. Archived from the original on 2020-10-28. Retrieved 2021-04-25 .
  22. ^ a b c d e f g Bergmann, Heinz (1982). "Verfahren zur Rauschminderung bei der Tonsignalverarbeitung" (PDF). radio fernsehen elektronik (rfe) (in German). Vol. 31, no. 11. Berlin, Deutschland: VEB Verlag Technik [de]. pp. 731–736 [731]. ISSN 0033-7900. Archived (PDF) from the original on 2021-05-05. Retrieved 2021-05-05 . p. 731: ExKo Breitband-Kompander Aufnahme/Wiedergabe 9 dB Tonband (NB. Page 736 is missing in the linked PDF.)
  23. ^ Haase, Hans-Joachim (August 1980). Written at Aschau, Germany. "Rauschunterdrückung: Kampf dem Rauschen". Systeme und Konzepte. Funk-Technik - Fachzeitschrift für Funk-Elektroniker und Radio-Fernseh-Techniker - Offizielles Mitteilungsblatt der Bundesfachgruppe Radio- und Fernsehtechnik (in German). Vol. 35, no. 8. Heidelberg, Germany: Dr. Alfred Hüthig Verlag GmbH [de]. pp. W293–W296, W298, W300 [W298, W300]. ISSN 0016-2825. Archived from the original on 2021-04-25. Retrieved 2021-04-25 . pp. W298, W300: […] Super-Dolby im Plus Due north 55 […] Der Kompander "Plus N55" arbeitet nach dem von Sanyo entwickelten Super-D-Noise-Reduction-System. Er ist speziell für 3-Kopf-Geräte konzipiert und den Pegelverhältnissen von japanischen Cassetten-Bandgeräten angepaßt. Für How-do-you-do-Fi-Anlagen, die ausschließlich 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 Bespeak-Verstärkung vornimmt. Dice ebenfalls im gesamten Tonfrequenzbereich wirksamen Kompressor/Expander-Funktionen sind in zwei Frequenz-Bereiche aufgeteilt (f0 ≈ 4,8 kHz), um jeweils ein optimales Arbeiten in diesen Bereichen zu gewährleisten […] 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 für das Bandrauschen beträgt hier rd. 10 dB […] Wird ein Pegel von −20 dB eingespeist, hebt der Encoder diesen auf einen Ausgangspegel von −ten dB an […] Am Decoder Eingang liegt nun - vom Bandgerät 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 zurück, wird es auf −30 dB angehoben und auch wieder um 30 dB expandiert. So wird das Bandrauschen immer um den jeweiligen Kompressions/Expansionsgrad unterdrückt. […] "Über Alles" gesehen stellen sich bei jedem Eingangspegel lineare Frequenzgänge 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 über den eingebauten Pegeltongenerator, wobei human being den Ausschlag der Fluoreszenz-Anzeige am Plus N55 und am Aussteuerungsanzeiger des Tonbandgerätes auf gleiche Werte (zum Beispiel −5 dB) einpegeln muß. Das ist ein einmaliger Vorgang bei gleichbleibender Gerätekombination. Danach wird die Aufnahme nur noch am Kompander ausgesteuert. […] Beachtenswert sind noch die Verzerrungen, die durch das Einfügen einer ganzen Anzahl von Transistorstufen in den Übertragungsweg zusätzlich entstehen. Das Diagramm […] zeigt dice frequenzabhängigen Klirrfaktoren bei Vollaussteuerung der beiden Encoder- und Decoder-Strecken im Plus N55. Im Vergleich zu linearen Verstärkern sind sie relativ hoch, gegenüber den im Bereich der Vollaussteuerung vorliegenden kubischen Klirrfaktoren bei Cassetten-Bändern aber noch vertretbar. […]
  24. ^ "Stereo Automat MK42 R-Player Budapesti Rádiótechnikai Gyár B". Archived from the original on 2021-04-25. Retrieved 2021-04-25 .
  25. ^ HIGH COM - The HIGH COM broadband compander utilizing the U401BR integrated circuit (PDF) (Semiconductor information 2.80). AEG-Telefunken. Archived (PDF) from the original on 2016-04-16. Retrieved 2016-04-16 .
  26. ^ Hoffman, Frank W. (2004). Encyclopedia of Recorded Sound. Vol. one (revised ed.). Taylor & Francis.
  27. ^ "Noise Reduction". Audiotools.com. 2013-11-x.
  28. ^ "Philips' Dynamic Noise Limiter". Archived from the original on 2008-eleven-05. Retrieved 2009-01-xiv .
  29. ^ "Dynamic Noise Reduction". ComPol Inc.
  30. ^ "History". Archived from the original on 2007-09-27. Retrieved 2009-01-14 .
  31. ^ "Sound Terms". Archived from the original on 2008-12-twenty. Retrieved 2009-01-14 .
  32. ^ "LM1894 Dynamic Racket Reduction System DNR". Archived from the original on 2008-12-20. Retrieved 2009-01-14 .
  33. ^ Gunyo, Ed. "Development of the Riviera - 1983 the 20th Ceremony". Riviera Owners Association. (NB. Originally published in The Riview, Vol. 21, No. vi, September/October 2005.)
  34. ^ http://www.hellodirect.com/catalog/Production.jhtml?PRODID=11127&CATID=15295 [ dead link ]
  35. ^ Boashash, B., ed. (2003). Time-Frequency Signal Analysis and Processing – A Comprehensive Reference. Oxford: Elsevier Science. ISBN978-0-08-044335-5.
  36. ^ Boashash, B. (April 1992). "Estimating and Interpreting the Instantaneous Frequency of a Betoken-Part I: Fundamentals". Proceedings of the IEEE. eighty (iv): 519–538. doi:ten.1109/5.135376.
  37. ^ 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, 2019.
  38. ^ Liu, Puyin; Li, Hongxing (2004). Fuzzy Neural Network Theory and Application. Intelligent Robots and Figurer Vision 13: Algorithms and Calculator Vision. Vol. 2353. World Scientific. pp. 303–325. Bibcode:1994SPIE.2353..303G. doi:10.1117/12.188903. ISBN978-981-238-786-viii. S2CID 62705333.
  39. ^ Chervyakov, Northward. I.; Lyakhov, P. A.; Nagornov, North. N. (2018-xi-01). "Quantization Noise of Multilevel Discrete Wavelet Transform Filters in Prototype Processing". Optoelectronics, Instrumentation and Data Processing. 54 (vi): 608–616. Bibcode:2018OIDP...54..608C. doi:10.3103/S8756699018060092. ISSN 1934-7944. S2CID 128173262.
  40. ^ Craciun, Thou.; Jiang, Ming; Thompson, D.; Machiraju, R. (March 2005). "Spatial domain wavelet design for feature preservation in computational data sets". IEEE Transactions on Visualization and Reckoner Graphics. 11 (2): 149–159. doi:10.1109/TVCG.2005.35. ISSN 1941-0506. PMID 15747638. S2CID 1715622.
  41. ^ Gajitzki, Paul; Isar, Dorina; Simu, Călin (Nov 2018). "Wavelets Based Filter Banks for Real Time Spectrum Analysis". 2018 International Symposium on Electronics and Telecommunications (ISETC): ane–4. doi:x.1109/ISETC.2018.8583929. ISBN978-1-5386-5925-0. S2CID 56599099.
  42. ^ a b Forouzanfar, M.; Abrishami-Moghaddam, H.; Ghadimi, S. (July 2008). "Locally adaptive multiscale Bayesian method for paradigm denoising based on bivariate normal changed Gaussian distributions". International Journal of Wavelets, Multiresolution and Information Processing. 6 (4): 653–664. doi:10.1142/S0219691308002562. S2CID 31201648.
  43. ^ Mallat, Due south. (1998). A Wavelet Tour of Signals Processing. London: Academic Printing.
  44. ^ Besag, Julian (1986). "On the Statistical Analysis of Dirty Pictures" (PDF). Journal of the Royal Statistical Society. Series B (Methodological). 48 (3): 259–302. doi:10.1111/j.2517-6161.1986.tb01412.x. JSTOR 2345426.
  45. ^ Seyyedi, Saeed (2018). "Incorporating a Noise Reduction Technique Into Ten-Ray Tensor Tomography". J IEEE Transactions on Computational Imaging. 4 (1): 137–146. doi:x.1109/TCI.2018.2794740. JSTOR 17574903. S2CID 46793582.
  46. ^ Dabov, Kostadin; Foi, Alessandro; Katkovnik, Vladimir; Egiazarian, Karen (xvi July 2007). "Image denoising past sparse 3D transform-domain collaborative filtering". IEEE Transactions on Image Processing. xvi (viii): 2080–2095. Bibcode:2007ITIP...xvi.2080D. CiteSeerXten.1.1.219.5398. doi:10.1109/TIP.2007.901238. PMID 17688213. S2CID 1475121.
  47. ^ Schmidt, Uwe; Roth, Stefan (2014). Shrinkage Fields for Effective Image Restoration (PDF). Reckoner Vision and Blueprint Recognition (CVPR), 2014 IEEE Conference on. Columbus, OH, USA: IEEE. doi:10.1109/CVPR.2014.349. ISBN978-1-4799-5118-5.
  48. ^ Ulyanov, Dmitry; Vedaldi, Andrea; Lempitsky, Victor (thirty Nov 2017). "Deep Prototype Prior". arXiv:1711.10925v2 [Vision and Pattern Recognition Figurer Vision and Design Recognition].

External links [edit]

  • Recent trends in denoising tutorial
  • Racket Reduction in photography
  • Matlab software and Photoshop plug-in for image denoising (Pointwise SA-DCT filter)
  • Matlab software for image and video denoising (Non-local transform-domain filter)
  • Not-local epitome denoising, with code and online demonstration

whitewitiet50.blogspot.com

Source: https://en.wikipedia.org/wiki/Noise_reduction

0 Response to "Software to Read Image Color on Frequencies"

Post a Comment

Subscribe to: Post Comments (Atom)

Iklan Atas Artikel

Iklan Tengah Artikel 1

Iklan Tengah Artikel 2

Iklan Bawah Artikel