Noise in an image is described as random granulation that is particularly visible in uniform areas.
Noise in an image can stem from several different factors:
Noise itself is characterized by several values, among which are Standard Deviation and Signal-to-Noise Ratio (SNR). Other measures are derived from these, such as Dynamic Range and Tonal Range. (Other aspects of interest include color sensitivity, and noise granulation and coloration, which will be discussed separately on this site.)
where is the mean gray level measured in the patch.
SNR is independent of any gain applied to the signal since signal and noise are equally amplified.
Dynamic range is defined as the ratio between the highest and lowest gray luminance a sensor can capture. However, the lowest gray luminance makes sense only if it is not drowned by noise, thus this lower boundary is defined as the gray luminance for which the SNR is larger than 1. The dynamic range is a ratio of gray luminance; it has no defined unit per se, but it can be expressed in Ev, or f-stops.
Tonal range is the effective number of gray levels the system can produce. This measure has to take noise into account (indeed, a very thin gray-level quantization is irrelevant if the quantization step is much smaller than noise). The standard deviation of noise can be viewed as the smallest difference between two distinguishable gray levels. The expression of the tonal range is
Since tonal range is a number with no unit, one can consider instead , which represents the number of bits necessary to encode all distinguishable gray levels.