Normalization (image processing)

In image processing, normalization is a process that changes the range of pixel 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 expansion. In image processing, normalization is a process that changes the range of pixel 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 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. Normalization transforms an n-dimensional grayscale image I : { X ⊆ R n } → { Min , . . , Max } {displaystyle I:{mathbb {X} subseteq mathbb {R} ^{n}} ightarrow {{ ext{Min}},..,{ ext{Max}}}} with intensity values in the range (Min,Max), into a new image I N : { X ⊆ R n } → { newMin , . . , newMax } {displaystyle I_{N}:{mathbb {X} subseteq mathbb {R} ^{n}} ightarrow {{ ext{newMin}},..,{ ext{newMax}}}} with intensity values in the range (newMin,newMax). The linear normalization of a grayscale digital image is performed according to the formula