This filter can also be used to reduce banding in feathered selections or graduated fills, to give a more realistic look to heavily retouched areas, or to create a textured layer. You can set the amount of noise, the type of noise distribution, and color mode. The Uniform option creates a subtle distribution appearance and Gaussian creates a speckled distribution look. Monochromatic applies the filter using the existing tones of the image without changing the colors. Despeckle The Despeckle filter detects the edges in a layer areas where significant color changes occur and blurs all of the selection except those edges. This blurring removes noise while preserving detail.
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Example of noise reduction using Audacity with 0 dB , 5 dB, 12 dB, and 30 dB reduction, Hz frequency smoothing, and 0. Problems playing this file? See media help. When using analog tape recording technology, they may 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.
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 or DNR work to reduce noise as it occurs, including both before and after the recording process as well as for live broadcast applications. Dual-ended systems have a pre-emphasis process applied during recording and then a de-emphasis process applied at playback.
The first widely used audio noise reduction technique was developed by Ray Dolby in The Dolby B system developed in conjunction with Henry Kloss was a single band system designed for consumer products. The Dolby B system, while not as effective as Dolby A, had the advantage of remaining listenable on playback systems without a decoder.
Blackmer , founder of dbx laboratories. 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. Dynamic noise limiter and dynamic noise reduction[ edit ] Dynamic noise limiter DNL is an audio noise reduction system originally introduced by Philips in for use on cassette decks. Its circuitry is also based on a single chip. GM cars introduced in 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,  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.
Software programs[ edit ] Most general purpose voice editing software will have one or more noise reduction functions Audacity , WavePad, etc. In images[ edit ] Images taken with both digital cameras and conventional film cameras will pick up noise from a variety of sources. Further use of these images will often require that the noise be partially removed — for aesthetic purposes as in artistic work or marketing , or for practical purposes such as computer vision. Types[ edit ] 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.
Tradeoffs[ edit ] 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[ edit ] 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.
Linear smoothing filters[ edit ] 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.
Main article: 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. Main article: 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[ edit ] A median filter is an example of a non-linear filter and, if properly designed, is very good at preserving image detail. 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[ edit ] The main aim of an image denoising algorithm is to achieve both noise reduction and feature preservation. 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.
To address these disadvantages, non-linear 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.
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