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Local Normalization
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LOCAL NORM.

ImageJ's plugin

# Local Normalization

## Filter to reduce the effect on a non-uniform illumination

Written by Daniel Sage at the Biomedical Image Group, EPFL, Switzerland

#### Outline

The local normalization tends to uniformize the mean and variance of an image around a local neighborhood. This is especially useful for correct uneven illumination or shading artifacts. Thanks to our fact implementation of the Gaussian filtering, the Local Normalization is running very fast.

#### Reference

D. Sage, M. Unser, Easy Java Programming for Teaching Image Processing Proceedings of the IEEE International Conference on Image Processing (ICIP'01), Thessaloniki, Hellenic Republic, 2001.

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TITLE="Easy {J}ava Programming for Teaching Image Processing",
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editor="",
volume="{III}",
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pages="298--301",
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#### Software

##### Description

The local normalization of f(x,y) is computed as follows:

 where: f(x,y) is the original image mf(x,y) is an estimation of a local mean of f(x,y) σf(x,y) is an estimation of the local variance g(x,y) is the output image

The estimation of the local mean and variance is performed through local spatial smoothing. In this implementation, we use fast recursive Gaussian filters. The parameters of the algorithm are the sizes of the smoothing windows, σ1, and, σ2, which control the estimation of the local mean and local variance, respectively. Often σ2 should be larger than σ1.

##### Pre-requirement

The software provided here is a plugin for ImageJ, a general purpose image-processing and image-analysis package. It runs also on the image-processing package Fiji. ImageJ has a public domain licence; it runs on several plateforms: Unix, Linux, Windows, and Mac OS X. It doesn't take more than a couple of minutes to install.

Download Local_Norm.jar [Version 17.01.2017]. The plugin consists in one single JAR file; place it into the "plugins" folder of ImageJ. Do not unzip the JAR file.
After restarting ImageJ, you have a new entrie in the Plugin » Filters of ImageJ, which is Local_Normalization.

##### Instruction
• Select your input image by set the focus on the image.
• Set the smoothing windows σ1, and σ2.
• Choose OK to process the image.
##### Conditions of use
• You are free to use this software for research purposes, but you should not redistribute it without our consent.
• In addition, we expect you to include adequate citations and acknowledgments whenever you present or publish results that are based on it.
##### Matlab version

Matlab version of the Local Normalization, by Guanglei Xiong, 17 Aug 2005 (Updated 07 Sep 2005)

#### Javascript Demonstration

We have also a demonstration of this algorithm running on any browser.

#### Example

 Input image Output image

• 20.12.2020