Each pixel in a camera sensor contains one or more light sensitive photodiodes which convert the incoming light (photons) into an electrical signal which is processed into the color value of the pixel in the final image. If the same pixel would be exposed several times by the same amount of light, the resulting color values would not be identical but have small statistical variations, called "noise". Even without incoming light, the electrical activity of the sensor itself will generate some signal, the equivalent of the background hiss of audio equipment which is switched on without playing any music. This additional signal is "noisy" because it varies per pixel (and over time) and increases with the temperature, and will add to the overall image noise. It is called the "noise floor". The output of a pixel has to be larger than the noise floor in order to be significant (i.e. to be distinguishable from noise).
Noise in digital images is most visible in uniform surfaces (such as blue skies and shadows) as monochromatic grain, similar to film grain (luminance noise) and/or as colored waves (color noise). As mentioned earlier, noise increases with temperature. It also increases with sensitivity, especially the color noise in digital compact cameras (example D below). Noise also increases as pixel size decreases, which is why digital compact cameras generate much noisier images than digital SLRs. Professional grade cameras with higher quality components and more powerful processors that allow for more advanced noise removal algorithms display virtually no noise, especially at lower sensitivities. Noise is typically more visible in the red and blue channels than in the green channel. This is why the unmagnified red channel crops in the examples below are better at illustrating the differences in noise levels.
|Blue Sky Crop||A||B||C||D||E|
|Camera Grade||Professional||Prosumer||Prosumer||Prosumer||Crop C after 123di noise reduction.|
|Red Ch. St. Dev.||1.8||2.5||5.6||22.6||1.4|
The standard deviation measured in a uniform area of an image (in the above examples measured in the red channel) is a good way to quantify image noise as it is an indication of how much the pixels in that area differ from the average pixel value in that area. The standard deviation in the noisy examples C and D is much larger than A, B, and E. Crop E shows that noise reduction can go a long way.
Another type of noise, often referred to as "stuck pixels" or "hot pixels" noise, occurs with long exposures (1-2 seconds or more) and appears as a pattern of colored dots (slightly larger than a single pixel). As explained in the noise reduction topic, long exposure noise is much less visible in the latest digital cameras.
|This article is written by Vincent Bockaert,|
author of The 123 of digital imaging Interactive Learning Suite
Click here to visit 123di.com