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 a characteristic of the particle size and texture used in the magnetic emulsion that is sprayed on the recording media.
The first really useful audio noise reduction technique was developed by Ray Dolby in the 1960s. It was a encode/decode system in which the volume of certain frequencies were increased during recording (encoding), then decreased proportionately during playback (decoding). In particular, when recording quiet parts of an audio signal, the high frequency part of the signal would have its volume increased. This had the effect of increasing the signal to noise ratio on tape (from say 10dB to 40dB depending on the initial signal volume). When it was played back, the decoder reversed the process, in effect reducing the noise level by 30dB.
An alternative analog noise reduction technique was developed by dBx laboratories. It was a much simpler process essentially using a root-mean-squared (RMS) encode/decode algorithm.
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 camera s 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:
- 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 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.
See also
Last updated: 05-03-2005 17:50:55
