In general[edit]

Noise reduction algorithms tend to alter signals to a greater or lesser degree. The local signal-and-noise orthogonalization algorithm can be used to avoid changes to the signals.[1]

In seismic exploration[edit]

Boosting signals in seismic data is especially crucial for seismic imaging,[2][3] inversion,[4][5] and interpretation,[6] thereby greatly improving the success rate in oil & gas exploration.[7][8][9][10] 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.

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

Filter (signal processing)

Signal processing

Signal subspace

Recent trends in denoising tutorial

Noise Reduction in photography

Matlab software and Photoshop plug-in for image denoising (Pointwise SA-DCT filter)

Matlab software for image and video denoising (Non-local transform-domain filter)

Non-local image denoising, with code and online demonstration