Noise reduction

All signal processing devices, both analog and digital, have traits that make them susceptible to noise.

These agitated electrons rapidly add and subtract from the output signal and thus create detectable noise.

In the case of photographic film and magnetic tape, noise (both visible and audible) is introduced due to the grain structure of the medium.

To compensate for this, larger areas of film or magnetic tape may be used to lower the noise to an acceptable level.

Noise reduction algorithms tend to alter signals to a greater or lesser degree.

[7][8][9] The useful signal that is smeared in the ambient random noise is often neglected and thus may cause fake discontinuity of seismic events and artifacts in the final migrated image.

Enhancing the useful signal while preserving edge properties of the seismic profiles by attenuating random noise can help reduce interpretation difficulties and misleading risks for oil and gas detection.

Single-ended hiss reduction systems (such as DNL[10] or DNR) work to reduce noise as it occurs, including both before and after the recording process as well as for live broadcast applications.

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.

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).

This had the effect of increasing the signal-to-noise ratio on tape up to 10 dB 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 10 dB.

[20] In various late-generation High Com tape decks the Dolby-B emulating D NR Expander functionality worked not only for playback, but, as an undocumented feature, also during recording.

dbx operated across the entire audible bandwidth and unlike Dolby B was unusable without a decoder.

[30][page needed] 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.

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,[34] 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.

One method to remove noise is by convolving the original image with a mask that represents a low-pass filter or smoothing operation.

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.

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.

The main aim of an image denoising algorithm is to achieve both noise reduction[37] and feature preservation[38] using the wavelet filter banks.

In the wavelet domain, the noise is uniformly spread throughout coefficients while most of the image information is concentrated in a few large ones.

[42][43] 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.

[45] Various deep learning approaches have been proposed to achieve noise reduction[46] and such image restoration tasks.

[47] Most general-purpose image and photo editing software will have one or more noise-reduction functions (median, blur, despeckle, etc.