Noise reduction

Last revised by Daniel J Bell on 19 May 2020

Citation, DOI, disclosures and article data

Citation:

Moore C, Bell D, Noise reduction. Reference article, Radiopaedia.org (Accessed on 25 Apr 2024) https://doi.org/10.53347/rID-72577

DOI:

https://doi.org/10.53347/rID-72577

Permalink:

https://radiopaedia.org/articles/72577

rID:

72577

Article created:

1 Dec 2019, Candace Makeda Moore

Disclosures:

At the time the article was created Candace Makeda Moore had no recorded disclosures.

View Candace Makeda Moore's current disclosures

Last revised:

19 May 2020, Daniel J Bell ◉

Disclosures:

At the time the article was last revised Daniel J Bell had no recorded disclosures.

View Daniel J Bell's current disclosures

Revisions:

2 times, by 2 contributors - see full revision history and disclosures

Sections:

Artificial Intelligence, Imaging Technology, Radiography

Synonyms:

Noise suppression
Denoising

Noise reduction, also known as noise suppression or denoising, commonly refers to the various algorithmic techniques to reduce noise in digital images once they are created although a few sources use the term more broadly to imply anything that reduces noise. In digital image processing various techniques, most of which are filtering techniques are applied to images at various stages after acquisition. These methods can involve both spatial filters (convolutions), frequency filters (discrete Fourier transform), morphological filters or even statistical filters.

Practical points

Many radiologists are familiar with CT reconstruction kernels and using a smooth kernel would be an example (in most cases) of noise reduction. The use of CT reconstruction kernels is often a choice, but some noise reduction techniques are automated and performed on raw imaging data without the radiologist necessarily being aware of it. Advanced noise reduction techniques are considered part of AI.