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Noise reduction is the process of removing noise from a signal. Noise reduction techniques exist for audio and images. Noise reduction algorithms may distort the signal to some degree. Noise rejection is the ability of a circuit to isolate an undesired signal component from the desired signal component, as with common-mode rejection ratio.
This category collects Wikipedia articles on techniques for removal or reduction of noise and artifacts from images and multi-dimensional data. Pages in category "Image noise reduction techniques" The following 19 pages are in this category, out of 19 total.
The high sensitivity image quality of a given camera (or RAW development workflow) may depend greatly on the quality of the algorithm used for noise reduction. Since noise levels increase as ISO sensitivity is increased, most camera manufacturers increase the noise reduction aggressiveness automatically at higher sensitivities. This leads to a ...
Noise reduction, the recovery of the original signal from the noise-corrupted one, is a very common goal in the design of signal processing systems, especially filters. The mathematical limits for noise removal are set by information theory .
In many cases noise found on a signal in a circuit is unwanted. There are many different noise reduction techniques that can reduce the noise picked up by a circuit. Faraday cage – A Faraday cage enclosing a circuit can be used to isolate the circuit from external noise sources. A Faraday cage cannot address noise sources that originate in ...
Such noise reduction is a typical pre-processing step to improve the results of later processing (for example, edge detection on an image). Median filtering is very widely used in digital image processing because, under certain conditions, it preserves edges while removing noise (but see the discussion below for which kinds of noise), also ...
From left: Original image, blurred image, image deblurred using Wiener deconvolution. In mathematics, Wiener deconvolution is an application of the Wiener filter to the noise problems inherent in deconvolution. It works in the frequency domain, attempting to minimize the impact of deconvolved noise at frequencies which have a poor signal-to ...
Sensors such as CCD, CMOS or ultrasonic probe may encapsulate noise signal. Noise reduction is commonly used to improve quality of the image. However, techniques such as smoothing filters and many other algorithms may lose local structure of image while denoising the image. [1] More over, efficiency is also taken into consideration.