Noise reduction
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.
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 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 luminace part (composite video signal in direct colour systems), which keeps the tape at saturation level, audio style noise reduction is unnecessary.
Modern digital sound (and picture) recordings no longer need to worry about tape hiss either 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.
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.
Types of noise
* In
salt and pepper noise (also known as
random noise 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 filters
One method to remove noise is by
convolving the original image with a mask. The Gaussian mask comprises elements determined by a
Gaussian function.It gives the image a blurred appearance if the standard deviation of the mask is high, 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 filtering works 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.
= Averaging
=Averaging is a degenerate case of Gaussian filtering, where the function defining the mask values has an infinite standard deviation.
Non-Linear filters
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:# consider each pixel in the image# sort the neighbouring pixels into order based upon their intensities# replace the original value of the pixel with the
median value from the listThis 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.
*
Noise*
Signal*
Signal processing*
Noise-cancelling headphone*
Digital image processing*
Noise print*
Understanding Image Noise - Types and Examples*
Noise Reduction by Averaging Images*
Noise Reduction in photography
