In image processing, normalization is a process that changes the range of

pixel In digital imaging, a pixel (abbreviated px), pel, or picture element is the smallest addressable element in a raster image, or the smallest point in an all points addressable display device. In most digital display devices, pixels are the ...

intensity values. Applications include photographs with poor contrast due to glare, for example. Normalization is sometimes called contrast stretching or histogram stretching. In more general fields of data processing, such as digital signal processing, it is referred to as

dynamic range Dynamic range (abbreviated DR, DNR, or DYR) is the ratio between the largest and smallest values that a certain quantity can assume. It is often used in the context of Signal (electrical engineering), signals, like sound and light. It is measured ...

expansion. The purpose of dynamic range expansion in the various applications is usually to bring the image, or other type of signal, into a range that is more familiar or normal to the senses, hence the term normalization. Often, the motivation is to achieve consistency in dynamic range for a set of data, signals, or images to avoid mental distraction or fatigue. For example, a newspaper will strive to make all of the images in an issue share a similar range of

grayscale In digital photography, computer-generated imagery, and colorimetry, a grayscale image is one in which the value of each pixel is a single sample representing only an ''amount'' of light; that is, it carries only intensity information. Graysc ...

. Normalization transforms an n-dimensional

image

I:\\rightarrow\

with intensity values in the range

(\text,\text)

, into a new image

I_N:\\rightarrow\

with intensity values in the range

(\text,\text)

. The

linear Linearity is the property of a mathematical relationship ('' function'') that can be graphically represented as a straight line. Linearity is closely related to '' proportionality''. Examples in physics include rectilinear motion, the linear ...

normalization of a

digital image is performed according to the formula :

I_N=(I-\text)\frac+\text

For example, if the intensity range of the image is 50 to 180 and the desired range is 0 to 255 the process entails subtracting 50 from each of pixel intensity, making the range 0 to 130. Then each pixel intensity is multiplied by 255/130, making the range 0 to 255. Normalization might also be non linear, this happens when there isn't a

relationship between

I

and

I_N

. An example of non-linear normalization is when the normalization follows a

sigmoid function A sigmoid function is a mathematical function having a characteristic "S"-shaped curve or sigmoid curve. A common example of a sigmoid function is the logistic function shown in the first figure and defined by the formula: :S(x) = \frac = \ ...

, in that case, the normalized image is computed according to the formula :

I_N=(\text-\text)\frac+\text

Where

\alpha

defines the width of the input intensity range, and

\beta

defines the intensity around which the range is centered.ITK Software Guide
/ref> Auto-normalization in image processing software typically normalizes to the full dynamic range of the number system specified in the image file format.

References

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External links

Contrast Stretching
Image processing

See also

References

External links