Noise clearly visible in this image (Panasonic Lumix LX1)

Image noise is a random, usually unwanted, variation in brightness or color information 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.

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.

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 at high exposure index (ISO speed) or slow shutter 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.

Types

Image with salt and pepper noise.

Salt-and-pepper noise

Fat-tail distributed or "impulsive" noise is sometimes called salt-and-pepper noise or spike noise.^[1]

An image containing salt-and-pepper noise will have dark pixels in bright regions and bright pixels in dark regions. This type of noise can be caused by dead pixels, analog-to-digital converter errors, bit errors in transmission, etc.^[2][3]

Shot noise

The dominant noise in the lighter parts of an image from an image sensor is typically that caused by statistical quantum fluctuations, that is, variation in the number of photons sensed at a given exposure level; this noise is known as photon shot noise.^[4] Shot noise has a root-mean-square value proportional to the square root of the image intensity, and the noises at different pixels are independent of one another. Shot noise follows a Poisson distribution, which is usually not very different from Gaussian.

In addition to photon shot noise, there can be additional shot noise from the dark leakage current in the image sensor; this noise is sometimes known as "dark shot noise"^[4] or "dark-current shot noise".^[5]

Amplifier noise (Gaussian noise)

The standard model of amplifier noise is additive, Gaussian, independent at each pixel and independent of the signal intensity, caused primarily by Johnson?Nyquist noise (thermal noise), including that which comes from the reset noise of capacitors ("kTC noise").^[6] In color cameras where more amplification is used in the blue color channel than in the green or red channel, there can be more noise in the blue channel.^[4]

Amplifier noise is a major part of the "read noise" of an image sensor, that is, of the constant noise level in dark areas of the image.^[7]

Quantization noise (uniform noise)

The noise caused by quantizing the pixels of a sensed image to a number of discrete levels is known as quantization noise; it has an approximately uniform distribution, and can be signal dependent, though it will be signal independent if other noise sources are big enough to cause dithering, or if dithering is explicitly applied.^[3]

Film grain

The grain of photographic film is a signal-dependent noise, related to shot noise.^[8] That is, if film grains are uniformly distributed (equal number per area), and if each grain has an equal and independent probability of developing to a dark silver grain after absorbing photons, then the number of such dark grains in an area will be random with a binomial distribution; in areas where the probability is low, this distribution will be close to the classic Poisson distribution of shot noise; nevertheless a simple Gaussian distribution is often used as an accurate enough model.^[3]

Film grain is usually regarded as a nearly isotropic (non-oriented) noise source, and is made worse by the distribution of silver halide grains in the film also being random.^[9]

Non-isotropic noise

Some noise sources show up with a significant orientation in images. For example, image sensors are sometimes subject to row noise or column noise.^[10] In film, scratches are an example of non-isotropic noise.

Image noise reduction

Noise cannot be removed without the loss of some information in the form of image detail. Nevertheless, noise-reduction algorithms have been developed to reduce noise without degrading image information too much.

Low- and high-ISO noise examples

<gallery caption="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." widths="150px" heights="315px" perrow="5"> Image:Flower_at_100_ISO_for_comparison.JPG|

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.

See also

• Noise reduction

References

1. ↑
2. ↑
3. ↑ ^{a b c}
4. ↑ ^{a b c}
5. ↑
6. ↑
7. ↑
8. ↑
9. ↑
10. ↑

External links

• Noise at dpreview glossary
• Compact Camera High ISO modes: Separating the facts from the hype

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