你可以看看噪声的说明，关于固定格式噪声，热噪声等等的公式，自然知道它的分布规律和模型！
cgQ4 JY/6 大部分的噪声最后反应都在图像上，这是从图像上我们常规的总结图像噪声的一些资料：
F8c^M</ Uz;z Gaussian noise
H pZD^h?L 0[f8Gb3 Main article: Gaussian noise
H=_ Wio Principal sources of Gaussian noise in digital images arise during acquisition e.g. sensor noise caused by poor illumination and/or high temperature, and/or transmission e.g. electronic circuit noise.[2]
0Gq}x;8H& A typical model of image noise is Gaussian, additive, 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").[3] 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.[4] 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.[5] At higher exposures, however, image sensor noise is dominated by shot noise, which is not Gaussian and not independent of signal intensity.
\:'|4D]'I {^Y0kvnd Salt-and-pepper noise
NZP>a V - 'aW}&!H M Main article: Salt and pepper noise
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w7(j SPB bv%A ; Image with salt and pepper noiseFat-tail distributed or "impulsive" noise is sometimes called salt-and-pepper noise or spike noise.[6] An image containing salt-and-pepper noise will have dark pixels in bright regions and bright pixels in dark regions.[7] This type of noise can be caused byanalog-to-digital converter errors, bit errors in transmission, etc.[8][9] It can be mostly eliminated by using dark frame subtraction and interpolating around dark/bright pixels.
>ZE8EL Dead pixels in an LCD monitor produce a similar, but non-random, display.[10]
S)$)AN<O .4t-5,7s% Shot noise
vE}>PEfA |#87|XIJ&~ The dominant noise in the lighter parts of an image from an image sensor is typically that caused by statistical quantum fluctuations, that is, variation in the number of photons sensed at a given exposure level. This noise is known as photon shot noise.[5] Shot noise has a root-mean-square value proportional to the square root of the image intensity, and the noises at different pixels are independent of one another. Shot noise follows a Poisson distribution, which is usually not very different from Gaussian.
<b5J"i&m In addition to photon shot noise, there can be additional shot noise from the dark leakage current in the image sensor; this noise is sometimes known as "dark shot noise"[5] or "dark-current shot noise".[11] Dark current is greatest at "hot pixels" within the image sensor. The variable dark charge of normal and hot pixels can be subtracted off (using "dark frame subtraction"), leaving only the shot noise, or random component, of the leakage.[12][13]If dark-frame subtraction is not done, or if the exposure time is long enough that the hot pixel charge exceeds the linear charge capacity, the noise will be more than just shot noise, and hot pixels appear as salt-and-pepper noise.
/^m3?q[a 6 4fB$ yY 3Mv/R Quantization noise (uniform noise)
lZt{L0 wDL dmrB 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. Though it can be signal dependent, it will be signal independent if other noise sources are big enough to cause dithering, or if dithering is explicitly applied.[9]
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"R8 J:N4F.o&K Film grain[edit]
R*DQm /5PV|o nO The grain of photographic film is a signal-dependent noise, with similar statistical distribution to shot noise.[14] 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. A simple Gaussian distribution is often used as an adequately accurate model.[9]
2tEA8F~k Film grain is usually regarded as a nearly isotropic (non-oriented) noise source. Its effect is made worse by the distribution of silver halide grains in the film also being random.[15]
3 ;N+5*- Ah_ T tj -F]0Py8( Anisotropic noise[edit]
O%$XgEJ8p !6yyX}%o Some noise sources show up with a significant orientation in images. For example, image sensors are sometimes subject to row noise or column noise.[16]
wva| TZ _olhCLIR- #S*@RKSE|7 In digital cameras[edit]
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tH }$j 7jf%-X 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, correct exposure requires the use of long shutter speeds, higher gain (ISO sensitivity), or both. On most cameras, longer shutter speeds lead to increased salt-and-pepper noise due to photodiode leakage currents. At the cost of a doubling of read noise variance (41% increase in read noise standard deviation), this salt-and-pepper noise can be mostly eliminated by dark frame subtraction. Banding noise, similar to shadow noise, can be introduced through brightening shadows or through color-balance processing.[17]
Q9\6P n ]T The relative effect of both read noise and shot noise increase as the exposure is reduced, corresponding to increased ISO sensitivity, since fewer photons are counted (shot noise) and since more amplification of the signal is necessary.
tW=,o&C= KBb {Z;% aS7%x>.A! Effects of sensor size[edit]
0zL7$Q#c jOE~?{8m The size of the image sensor, or effective light collection area per pixel sensor, is the largest determinant of signal levels that determine signal-to-noise ratio and hence apparent noise levels, assuming the aperture area is proportional to sensor area, or that the f-number or focal-plane illuminance is held constant. That is, for a constant f-number, the sensitivity of an imager scales roughly with the sensor area, so larger sensors typically create lower noise images than smaller sensors. In the case of images bright enough to be in the shot noise limited regime, when the image is scaled to the same size on screen, or printed at the same size, the pixel count makes little difference to perceptible noise levels – the noise depends primarily on sensor area, not how this area is divided into pixels. For images at lower signal levels (higher ISO settings), where read noise (noise floor) is significant, more pixels within a given sensor area will make the image noisier if the per pixel read noise is the same.
UTThl2=+ For instance, the noise level produced by a Four Thirds sensor at ISO 800 is roughly equivalent to that produced by a full frame sensor (with roughly four times the area) at ISO 3200, and that produced by a 1/2.5" compact camera sensor (with roughly 1/16 the area) at ISO 100. This ability to produce acceptable images at higher sensitivities is a major factor driving the adoption of DSLR cameras, which tend to use larger sensors than compacts. An example shows a DSLR sensor at ISO 400 creating less noise than a point-and-shoot sensor at ISO 100.[18]
=L UDg7P dV:vM9+x DaK 2P;WP Sensor fill factor[edit]
x c[BQ|P= Xyf7sHQ The image sensor has individual photosites to collect light from a given area. Not all areas of the sensor are used to collect light, due to other circuitry. A higher fill factor of a sensor causes more light to be collected, allowing for better ISO performance based on sensor size.[19]
w<4,;FFlZ/ z .xOT;t =V ctG>ct| Sensor heat[edit]
'(q VA>S 'u%;5;%2 Temperature can also have an effect on the amount of noise produced by an image sensor due to leakage. With this in mind, it is known that DSLRs will produce more noise during summer than winter.
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