Image noise
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Image noise is a random, usually unwanted, fluctuation of pixel values in an image. Image noise can originate in film grain, or in electronic noise in the input device (scanner or digital camera) sensor and circuitry, or in the unavoidable shot noise of an ideal photon detector.
Image noise is most apparent in image regions with low signal level, such as shadow regions or underexposed images.
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[edit] Useful noise
High levels of noise are almost always undesirable, but there are cases when lower levels of noise may be useful, for example to prevent discretization artifacts (color banding or posterization). Noise purposely added for such purposes is called dither.
[edit] Noise problems with digital cameras
In low light, or at high film speed ("ISO") settings, digital cameras sometimes produce unacceptable image noise. The two examples show a typical difference (best seen in full-size) between a well-lit subject and one in less light.
[edit] Image noise removal
Noise cannot be removed without some loss of information. Nevertheless, algorithms can be developed to analyse an image and determine whether pixels are likely to be due to noise, in which case they can be replaced by what the image is expected to look like. For example, a black speck on a photograph of a faraway grey wall can be replaced by the same grey as the rest of the wall; whether it was a fly on the wall or random noise can not be known with certainty.
[edit] Linear filters
One method to remove noise is by convolving the original image with a blurring mask such as a Gaussian function. Such a filter gives the image a blurred appearance if the width of the mask is high, and has the effect of smearing out the value of a single pixel over an area of the image. This brings the value of each pixel into closer harmony with the value of its neighbours. Linear filtering reduces noise, but the blurring also reduces resolution which can be a major drawback.
[edit] Non-Linear filters
A median filter is an example of a non-linear filter that is fairly good at preserving image detail. To run a median filter:
- consider each pixel in the image
- sort the neighbouring pixels into order based upon their intensities
- replace the original value of the pixel with the median value from the list
This type of filter is very good at removing salt and pepper noise from an image, and causes relatively little blurring of edges, and hence is often used in computer vision applications.
