خفض الضوضاء

هذا المقال يتضمن أسماءً أعجمية تتطلب حروفاً إضافية (پ چ ژ گ ڤ ڠ).
لمطالعة نسخة مبسطة، بدون حروف إضافية

خفض الصوت Noise reduction، هي عملية إزالة الضوضاء من الإشارة.

جميع أجهزة التسجيل، سواء التنظارية أو الرقمية، بها خصائص تجعلها عرضة للضوضواء. قد تكون الضوضاء عشوائية أو ضوضاء بيضاء غير مترابطة، أو ضوضاء غير مترابطة سببها ميكنة الجهاز أو معالجة الخوارزميات.

في أجهزة التسجيل الإلكترونية، الشكل الرئيسي من الضوضاء هو الهسهسة والتي تسببه الإلكترونات العشوائية التي، تتدفق بقوة بسبب الحرارة، فتبتعد عن المسار المحدد لها. تؤثر تلك الإلكترونات على جهد الإشارة الناتجة ومن ثم تسبب الضوضاء.

في حالة الفيلم الفوتوغرافي والشريط المغناطيسي، الضوضاء (المرئية والمسموعة) سببها البنية الحبيبية للوسط. في الفيلم الفوتوغرافي، حجم الحبيبات في الفيلم يحدد حساسية الفيلم، والفيلم الأكثر حساسية به حبيبات أكبر حجماً. في الشريط المغناطيسي، الحبيبات الأكبر في الجسيمات المغناطيسية (عادة أكسيد الحديديك) أو المگنيتايت)، تزيد من تعرض الوسط للضوضاء.

عوضاً عن ذلك، فالمساحات الكبيرة من الفيلم أو الشريط المغناطيسي قد تستخدم لتقليل الضوضاء إلى المستوى المقبول.


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في الصوت

عند استخدام تقنية التسجيل بالشريط التناظري، قد يكون التسجيل عرضة لنوع من الضوضاء يعرف بهسهسة الشريط. هذه الضوضاء مرتبطة بحجم الجسيم والتركيب المستخدم للطبقة الحساسة المغناطيسية التي تم رشها على وسيلة التسجيل، وأيضاً بالسرعة النسبية للشريط على رؤوس الشرائط.

هناك 4 أنواع من خفض الضوضاء: single-ended pre-recording, single-ended hiss reduction, single-ended surface noise reduction, and codec or dual-ended systems. Single-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 DNL or DNR) 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 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 B, Dolby C, Dolby S, dbx Type I and dbx Type II, High Com and High Com II as well as Toshiba's adres (ja) and JVC's ANRS (ja)) have a pre-emphasis process applied during recording and then a de-emphasis process applied at playback.


نظام دولبي دي بي إكس لخفض الضوضاء

في الوقت الذي توجد فيه عشرات الأنواع من خفض الصوت، فتقنية الصوت الأكثر استخداما هي التي طورها راي دوبلي عام 1966. بهدف استخدامها مهنياً، كان نظام دولبي، هو نظام تشفير/فك تشفير يتم فيه زيادة سعة الترددات في النطاقات الأربعة أثناء التسجيل (التشفير)، ثم الخفض التناسبي أثناء التشغيل (فك التشفير). كان نظام دولبي (تطور بالتزامن مع هنري كلوس)، كان نظام نظام إشارة تم تصميمه للسلع الاستهلاكية. بصفة خاصة، عند تسجيل أجزاء صامتة على إشارة الصوت، الترددات أعلى من 1 هرتز ستقوى. يسبب هذا زيادة الإشارة إلى معدل الضوضاء على الشريط إلى أكثر من 10dB حسب الإشارة الأولية. نظام دولبي بي، وهو ليس فعالاً كما دولبي إيه، 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-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 as an open ended system. However it could achieve up to 30 dB of noise reduction. Since 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.


محدد الضوء الديناميكي وخفض الضوضاء الديناميكية

Dynamic Noise Limiter (DNL) is an unpatented audio noise reduction system originally introduced by Philips in 1971 for use on cassette decks. Its circuitry is based on a single chip.[1]

It was further developed into Dynamic Noise Reduction (DNR) by National Semiconductor to reduce noise levels on long-distance telephony.[2] First sold in 1981, DNR is frequently confused with the far more common Dolby noise reduction system.[3] However, unlike Dolby and dbx Type I & Type II noise reduction systems, DNL and DNR are playback-only signal processing systems that do not require the source material to first be encoded, and they can be used together with other forms of noise reduction.[4]

Because DNL and DNR are non-complementary, meaning they do not require encoded source material, they can be used to remove background noise from any audio signal, including magnetic tape recordings and FM radio broadcasts, reducing noise by as much as 10 dB.[5] 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 system to mistrack.

The Telefunken High Com integrated circuit U401BR could be utilized to work as a Dolby B-compatible DNR-style expander as well.[6]

One of DNR's first widespread applications was in the GM Delco car stereo systems in U.S. GM cars introduced in 1984.[7] It 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 noise reduction system in microphone systems.[8]

وسائل أخرى

A second class of algorithms work in the time-frequency domain using some linear or non-linear filters that have local characteristics and are often called time-frequency filters.[9] 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 by using the mouse with a pen that has a defined time-frequency shape. This is done 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 "wanted noise", will be untouched. The region is typically defined by the location of the signal Instantaneous Frequency,[10] as most of the signal energy to be preserved is concentrated about it.

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

في الصور

Images taken with both digital cameras and conventional film cameras will pick up noise from a variety of sources. Many further uses of these images require that the noise will be (partially) removed - for aesthetic purposes as in artistic work or marketing, or for practical purposes such as computer vision.


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الأنواع

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 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 CCD 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 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 values at different pixels are modeled as being independent and identically distributed, and hence uncorrelated.

الإزالة

المفاضلات

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

فصل ضوضاء الصفاء والاستضاءة

In 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 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, 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 algorithms 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.

مرشحات التجانس الخطي

One method to remove noise is by convolving the original image with a mask that represents a low-pass filter or smoothing operation. For example, the Gaussian mask comprises elements determined 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 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 neighborhood 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.


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تجمع الإشارة

Two measurements of the same physical quantity often exhibit different 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-suppression approaches such as filtering or smoothing.[11] Noise reduction by signal combination has found applications in in-car microphone systems, single molecule biophysics, chemometrics among other disciplines.

Anisotropic diffusion

Another method for removing 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 designed to detect edges, the noise can be removed without blurring the edges of the image.

وسائل غير محلية

Another 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 the degree of similarity between a small patch centered around that pixel and the small patch centered around the pixel being de-noised.

مرشحات غير خطية

A 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. 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 the median value 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;[12] 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.

برمجيات

Most general purpose image and photo editing software will have one or more noise reduction functions (median, blur, despeckle, etc.). Special purpose noise reduction software programs include CreamyPhoto, Neat Image, Noiseware, Grain Surgery, Noise Ninja, DenoiseMyImage, Smart Image Denoiser, GREYCstoration (now G'MIC), and pnmnlfilt (nonlinear filter) found in the open source Netpbm tools. General purpose image and photo editing software including noise reduction functions include Adobe Photoshop, GIMP, PhotoImpact, Paint Shop Pro, Helicon Filter, and Darktable.[13]

انظر أيضاً

موضوعات عامة في الصوت

الصوت

الڤيديو

المصادر

  1. ^ [1] [2]
  2. ^ http://www.compolinc.com/dynamic.htm
  3. ^ http://www.national.com/company/pressroom/history80.html
  4. ^ http://www.triadspeakers.com/education_avterms.html
  5. ^ http://www.national.com/pf/LM/LM1894.html
  6. ^ AEG-Telefunken. HIGH COM - The HIGH COM broadband compander utilizing the U401BR integrated circuit. Semiconductor information 2.80 ([3]).
  7. ^ http://www.rivowners.org/features/evolution/evpt83.html
  8. ^ http://www.hellodirect.com/catalog/Product.jhtml?PRODID=11127&CATID=15295
  9. ^ B. Boashash, editor, "Time-Frequency Signal Analysis and Processing – A Comprehensive Reference", Elsevier Science, Oxford, 2003; ISBN 0-08-044335-4
  10. ^ B. Boashash, "Estimating and Interpreting the Instantaneous Frequency of a Signal-Part I: Fundamentals", Proceedings of the IEEE, Vol. 80, No. 4, pp. 519-538, April 1992, DOI:10.1109/5.135376
  11. ^ Mashaghi et al. Noise reduction by signal combination in Fourier space applied to drift correction in optical tweezers, Rev. Sci. Instrum. 82, 115103 (2011)
  12. ^ Puyin Liu and Hongxing Li (2004). Fuzzy Neural Network Theory and Application. World Scientific. ISBN 981-238-786-2.
  13. ^ profiling sensor and photon noise .. and how to get rid of it

وصلات خارجية