Normalization (image processing): Difference between revisions

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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]].

Normalization transforms an n-dimensional [[grayscale]] image

<math>I:\{\mathbb{X}\subseteq\mathbb{R}^n\}\rightarrow\{\text{Min},..,\text{Max}\}</math>

with intensity values in the range <math>(\text{Min},\text{Max})</math>, into a new image

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with intensity values in the range <math>(\text{newMin},\text{newMax})</math>.

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

:<math>I_N=(I-\text{Min})\frac{\text{newMax}-\text{newMin}}{\text{Max}-\text{Min}}+\text{newMin}</math>

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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, thisas happens when there isn't a [[linear]]the relationship between <math>I</math> and <math>I_N</math> may not be [[linear]]. An example of non-linear normalization is when the normalization follows a [[sigmoid function]], in thatwhich case, the normalized image is computed according to the formula

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

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== Contrast Stretching for Image Enhancement ==

This is the most significant and essential technique of spatial -based image enhancement.<ref>{{Cite web |title=Contrast Enhancement Techniques: A Brief and Concise Review |url=https://www.irjet.net/archives/V4/i7/IRJET-V4I7375.pdf}}</ref> The basic intent of the '''contrast enhancement technique''' is to adjust the local contrast in the image so as to bring out the clear regions or objects in the image . Low-contrast images often result from poor or non-uniform lighting conditions, a limited dynamic range of the [[imaging sensor]], or improper settings of the lens aperture.

[[File:Contrast_Stretching_Transformation_Functions.png|thumb|Contrast Stretching Transformation Functions]]

The '''contrast enhancement''' tries to change the intensity of the pixel in the image, particularly in the input image for the purpose, to obtain a morean enhanced image. .It is based on the number of techniques, namely local, global, dark and bright levels of contrast. .The contrast enhancement is considered as the amount of color or gray differentiation that lies among the different features in an image . The contrast enhancement improves the quality of image by increasing the luminance difference between the foreground and backgroundsbackground.

A '''Contrast Stretching Transformation''' can be achieved by: