Normalization (image processing): Difference between revisions

Revision as of 06:08, 18 May 2012

Revision as of 06:14, 18 May 2012

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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]].<br />

Normalization process that transform a n-dimensional [[grayscale]] image <math>I:\{1,.,\mathbb{X}\subseteq\mathbb{R}^n\}\rightarrow\{\text{Min},..,\text{Max}\}</math> with intensity values in the range (Min,Max), into a new image <math>I_N:\{1,.,\mathbb{X}\subseteq\mathbb{R}^n\}\rightarrow\{\text{newMin},..,\text{newMax}\}</math> with intensity values in the range (newMin,newMax). <br />

The [[linear]] normalization of a [[grayscale]] [[digital image]] is performed according to the formula

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:<math>I_N=(\text{newMax}-\text{newMin})\frac{1}{1+e^{\frac{I-\beta}{\alpha}}}+\text{newMin}</math>

Where <math>\alpha</math> defines the width of the input intensity range, and <math>\beta</math> defines the intensity around which the range is centered <ref>[http://www.itk.org/ItkSoftwareGuide.pdf ITK Software Guide]</ref>. <br />

Auto-normalization in image processing software typically normalizes to the full dynamic range of the number system specified in the image file format. <br />

Auto-normalization in image processing software typically normalizes to the full dynamic range of the number system specified in the image file format. <br />

The normalization process will produce iris regions, which have the same constant dimensions, so that two photographs of the same iris under different conditions will have characteristic features at the same spatial ___location.