Noise reduction

From Academic Kids

Noise reduction is the process of removing noise from a signal. Noise reduction techniques are conceptually very similar regardless of the signal being processed, however a priori knowledge of the characteristics of an expected signal can mean the implementations of these techniques vary greatly depending on the type of signal.

("Noise reduction" can also refer to the unrelated process of soundproofing.)


Audio noise reduction

When using analog technology, sound recordings exhibit a type 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 also to the relative tape velocity across the tape heads.

The first widely used audio noise reduction technique was developed by Ray Dolby 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). The Dolby B system (developed in conjunction with Henry Kloss) was a single band system designed for 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 process, 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-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 bandwith and unlike Dolby B was unusable as an open ended system. However it could achieve up to 30 dB of noise reduction.

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 dither systems actually add noise to a signal to improve its quality.

Image noise reduction

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 been (partially) removed - for aesthetic purposes as in artistic work or marketing, or for practical purposes such as computer vision.

Types of noise

  • In salt-and-pepper noise (also known as random or independent noise), pixels in the image are vastly different in color from their surrounding pixels. The defining characteristic is that the color 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 on the lens or inside the camera, or with digital cameras, faulty CCD elements.
  • In Gaussian noise (dependent noise), an amount of noise is added to every part of the picture. Each pixel in the image will be changed from its original value by a (usually) small amount. Taking a plot of the amount of distortion of a pixel against the frequency with which it occurs produces a Gaussian distribution of noise.

Removing image noise

Gaussian masks

One method to remove noise is by convolving the original image with a mask. The Gaussian mask gives an image a blurred appearance, and has the effect of smearing out the value of a single pixel over an area of the image. This brings the value of each pixel into closer harmony with the value of its neighbours. Gaussian masks work relatively well, but the blurring of edges can cause problems, particularly if the output is being fed into edge detection algorithms for computer vision applications.

Median filters

A median filter 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

This type of filter is very good at removing salt and pepper noise from an image, and also causes very little blurring of edges, and hence is often used in computer vision applications.


Another method to remove noise is by averaging several images. Averaging has the power to reduce noise without compromising detail, because it actually increases the signal-to-noise ratio (SNR) of your image. The amplitude of any random noise decreases as the square root of the number of images averaged.

See also

External links


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