Image noise

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Noise clearly visible in this image (Panasonic Lumix LX1)

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.

1 Useful noise
2 Noise problems with digital cameras
3 Image noise removal
- 3.1 Linear filters
- 3.2 Non-Linear filters
4 Low- and high-ISO noise examples
- 4.1 Video noise
5 See also
6 External links

[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

Image on the left has exposure time of >10 seconds in low light. The image on the right has adequate lighting and 0.1 second exposure.

In low light, or at high exposure index (ISO speed) settings, digital cameras tend to produce images with more apparent image noise. The two examples show a typical difference (best seen at full-size) between a well-lit subject and one in low light.

[edit] Image noise removal

Noise cannot be removed without the loss of some information in the form of image detail. Nevertheless, noise-reduction algorithms have been developed to analyse an image and determine whether unduly dark or light pixels are, in fact, details or are more likely to be due to noise.

[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 definition which can be a major drawback to image quality.

[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.

[edit] Low- and high-ISO noise examples

Images of a flower taken at ISO 100 and ISO 1600 on a Canon 400D digital camera. Both images were shot under similar lighting conditions, varying only the ISO setting and shutter speed.
ISO 100, f/5.6, 1/350 s (click on image for larger version)	ISO 1600, f/5.6, 1/4000 s (click on image for larger version)	Comparison of both images. This is a crop of a small section of each image displayed at 100%. The top portion was shot at 100 ISO, the bottom portion at 1600 ISO.

[edit] Video noise

In video and television, noise refers to the random dot pattern that is superimposed on the picture as a result of electronic noise, the 'snow' that is seen with poor (analog) television reception or on VHS tapes. Interference and static are other forms of noise, in the sense that they are unwanted, though not random, which can affect radio and television signals.