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I wonder if IPP supports Non Linear normalization of an image? Please take a look at:

http://en.wikipedia.org/wiki/Normalization_(image_processing)

for more details.

Best regards,

Sergey

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*Hi everybody,*

I wonder if IPP supports Non Linear normalization of an image? Please take a look at:

http://en.wikipedia.org/wiki/Normalization_(image_processing)

for more details.

I wonder if IPP supports Non Linear normalization of an image? Please take a look at:

http://en.wikipedia.org/wiki/Normalization_(image_processing)

for more details.

I'd like to make a newfeature request:

a function in Imageor Digital Signal Processing domainsfor a Non Linear Normalization of an image or a data set.

Best regards,

Sergey

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*Or, if you can generate the displacements by some means (Optical flow etc...), you can definitely apply the deformations*

**using the Warp functionality (ippiWarp....)**

*. on a displaced coordinate list. This is the closest one can get to a non-linear deformation of an image in ipp, currently.*

Thanks for the advise, Siddy! Actually, I don't need to transform an image geometrically. I need to reduce noise

in a very noisyimage and, at the same time,toenhance some details. To be honest, I've asked another person to evaluate

the equationfor Non-Linear Normalization ( from Wiki )and we have very strange results. I'll follow up with our R&D results

some time later...

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*...I've asked another person to evaluate the equationfor Non-Linear Normalization ( from Wiki )and*

we have very strange results. I'll follow up with our R&D results some time later...

we have very strange results. I'll follow up with our R&D results some time later...

Here is an example andfrom top to bottom are

Source set of "pixels", thenLinear Normalized and followed by Non-Linear Normalized:

Non-Linear Normalization looks like averaging( very similar to exponential averaging ). There is a question about

origins of the equation for Non-Linear Normalization.

[**EDITED**] Please see a **Post #7**. Unfortunately, an error was detected in the equation for Non-Linear Normalization.

It finally explainswhy we hadstrangeresults.

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*...Non Linear normalization of an image? Please take a look at:*

http://en.wikipedia.org/wiki/Normalization_(image_processing)

http://en.wikipedia.org/wiki/Normalization_(image_processing)

**Attention**: I'd like to to inform that the equation for Non-Linear Normalization of an imagehas an error.

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**Source set**of "pixels", then

**Linear Normalized**and followed by

**Non-Linear Normalized**( Alpha=0.5 & Beta=2.5 ):

I hope that it will be useful for a software developer or a researcher working on an image processing project.

Best regards,

Sergey

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**Example of Linear andNon-Linear Normalization**

Single-Precision Data Set (size: 1 x 16 )

>> **Original Data** <<

Range: Min = 1.000000 Max = 6.000000

1.000000 1.000000 2.000000 2.000000 1.000000 1.000000 4.000000 4.000000 1.000000 1.000000 6.000000 6.000000 1.000000 1.000000 1.000000 1.000000

>> **Linear Normalized Data** <<

New Range: Min = 0.000000 Max = 24.000000

0.000000 0.000000 4.800000 4.800000 0.000000 0.000000 14.400001 14.400001 0.000000 0.000000 24.000000 24.000000 0.000000 0.000000 0.000000 0.000000

>> **Non-Linear Normalized Data** <<

New Range: Min = 0.000000 Max = 24.000000

**Test 1**:

Params: Alpha = 0.032000 Beta = 2.500000

0.000000 0.000000 0.000004 0.000004 0.000000 0.000000 24.000000 24.000000 0.000000 0.000000 24.000000 24.000000 0.000000 0.000000 0.000000 0.000000

**Test 2**:

Params: Alpha = 0.064000 Beta = 2.500000

0.000000 0.000000 0.009708 0.009708 0.000000 0.000000 24.000000 24.000000 0.000000 0.000000 24.000000 24.000000 0.000000 0.000000 0.000000 0.000000

**Test 3**:

Params: Alpha = 0.125000 Beta = 2.500000

0.000147 0.000147 0.431669 0.431669 0.000147 0.000147 23.999853 23.999853 0.000147 0.000147 24.000000 24.000000 0.000147 0.000147 0.000147 0.000147

**Test 4**:

Params: Alpha = 0.250000 Beta = 2.500000

0.059343 0.059343 2.860870 2.860870 0.059343 0.059343 23.940657 23.940657 0.059343 0.059343 23.999981 23.999981 0.059343 0.059343 0.059343 0.059343

**Test 5**:

Params: Alpha = 0.500000 Beta = 2.500000

1.138221 1.138221 6.454594 6.454594 1.138221 1.138221 22.861778 22.861778 1.138221 1.138221 23.978134 23.978134 1.138221 1.138221 1.138221 1.138221

**Test 6**:

Params: Alpha = 0.750000 Beta = 2.500000

2.860870 2.860870 8.141848 8.141848 2.860870 2.860870 21.139130 21.139130 2.860870 2.860870 23.776417 23.776417 2.860870 2.860870 2.860870 2.860870

**Test 7**:

Params: Alpha = 1.000000 Beta = 2.500000

4.378212 4.378212 9.060976 9.060976 4.378212 4.378212 19.621788 19.621788 4.378212 4.378212 23.296507 23.296507 4.378212 4.378212 4.378212 4.378212

[**EDITED**] Added an example of Linear Normilized data

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