Tape hiss is a performance-limiting issue in
analog tape recording. 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. Four types of noise reduction exist: 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 It was further developed into
dynamic noise reduction (
DNR) by
National Semiconductor to reduce noise levels on long-distance
telephony. First sold in 1981, DNR is frequently confused with the far more common
Dolby noise-reduction system. Unlike Dolby and
dbx Type I and
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. 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. They can also 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. One of DNR's first widespread applications was in the
GM Delco car stereo systems in US GM cars introduced in 1984. 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.
Other approaches A second class of algorithms work in the time-frequency domain using some
linear or
nonlinear filters that have local characteristics and are often called
time-frequency filters. 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, much like in a paint program, by 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's instantaneous frequency, as most of the signal energy to be preserved is concentrated about it. Yet another approach is the automatic noise limiter and
noise blanker commonly found on
HAM radio transceivers,
CB radio transceivers, etc. Both of the aforementioned filters can be used separately or in conjunction with each other at the same time, depending on the transceiver itself.
Software programs Most
digital audio workstations (DAWs) and
audio editing software have one or more noise reduction functions. == In images == Images taken with
digital cameras or conventional
film cameras will pick up noise from a variety of sources. Further use of these images will often require that the noise be reduced either for aesthetic purposes or for practical purposes such as
computer vision.
Types In
salt and pepper noise (sparse light and dark disturbances), also known as impulse noise,
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. When viewed, the image contains dark and white dots, hence the term salt and pepper noise. Generally, this type of noise will only affect a small number of image pixels. 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.
Removal Tradeoffs There are many noise reduction algorithms in image processing. 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
Chroma and luminance noise separation In real-world photographs, the highest spatial-frequency detail consists mostly of variations in brightness (
luminance detail) rather than variations in hue (
chroma detail). Most photographic noise reduction algorithms split the image detail into chroma and luminance components and apply more noise reduction to the former or allow the user to control chroma and luminance noise reduction separately.
Linear smoothing filters 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
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.
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.
Non-local means 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 on that pixel and the small patch centered on the pixel being de-noised.
Nonlinear filters A
median filter is an example of a nonlinear 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 list A median filter is a rank-selection (RS) filter, a particularly harsh member of the family of rank-conditioned rank-selection (RCRS) filters; 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.
Wavelet transform The main aim of an image denoising algorithm is to achieve both noise reduction and feature preservation using the wavelet filter banks. In this context, wavelet-based methods are of particular interest. In the wavelet domain, the noise is uniformly spread throughout coefficients while most of the image information is concentrated in a few large ones. Therefore, the first wavelet-based denoising methods were based on thresholding of detail subband coefficients. However, most of the wavelet thresholding methods suffer from the drawback that the chosen threshold may not match the specific distribution of signal and noise components at different scales and orientations. To address these disadvantages, nonlinear estimators based on Bayesian theory have been developed. In the Bayesian framework, it has been recognized that a successful denoising algorithm can achieve both noise reduction and feature preservation if it employs an accurate statistical description of the signal and noise components.
Block-matching algorithms A
block-matching algorithm can be applied to group similar image fragments of 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.
Random field Shrinkage fields is a
random field-based
machine learning technique that brings performance comparable to that of
Block-matching and 3D filtering yet requires much lower computational overhead such that it can be performed directly within
embedded systems.
Deep learning Various
deep learning approaches have been proposed to achieve noise reduction and such
image restoration tasks.
Deep Image Prior is one such technique that makes use of
convolutional neural network and is notable in that it requires no prior training data.
Software Most general-purpose image and photo editing software will have one or more noise-reduction functions (median,
blur, despeckle, etc.). == See also ==