大家都知道，CMOS的噪聲有讀出噪聲，暗電流噪聲，固定模式噪聲等，那么請問這些噪聲如果用數(shù)學(xué)模型來解釋的話是按照什么來分布的呢？比如說，讀出噪聲好像是服從高斯分布的。

你可以看看噪聲的說明，關(guān)于固定格式噪聲，熱噪聲等等的公式，自然知道它的分布規(guī)律和模型！ C:J;'[,S  
大部分的噪聲最后反應(yīng)都在圖像上，這是從圖像上我們常規(guī)的總結(jié)圖像噪聲的一些資料： Y7}>yC/GY  
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Gaussian noise ^}=)jLS  
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Main article: Gaussian noise >S +}  
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] FbE/x$;~O  
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. u<BU4c/
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Salt-and-pepper noise iNA3Y  
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Main article: Salt and pepper noise '8X>,un  
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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. ebk>e*  
Dead pixels in an LCD monitor produce a similar, but non-random, display.[10] IK2da@V  
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Shot noise s:U:Dv  
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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. bBjr hi  
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. !/is+ xp  
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Quantization noise (uniform noise) 1X?ro;  
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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] 7*5B  
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Film grain[edit] $p0nq&4c  
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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] YJJ1N/Z1  
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] |`T(:ZKXZ2  
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Anisotropic noise[edit] H%wB8Y ]  
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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] }%< ?]  
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In digital cameras[edit] 9z7_D_yN2  
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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] *[Hp&6f  
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. n1-p/a.  
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Effects of sensor size[edit] noZbsI4  
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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. 'oa.-g 沁阳市| 理塘县| 桐乡市| 同心县| 禄丰县| 辰溪县| 西昌市| 洱源县| 珠海市| 青神县| 武功县| 南部县| 东阳市| 丹东市| 中山市| 上蔡县| 九龙坡区| 浦东新区| 囊谦县| 南靖县| 栖霞市| 常山县| 双桥区| 响水县| 灵川县| 略阳县| 青神县| 长宁区| 玉屏| 赤峰市| 海兴县| 土默特右旗| 故城县| 阿合奇县| 阿克苏市| 辽宁省| 枣庄市| 石家庄市| 思茅市| 桑日县| 阜平县|