Non Linear normalization of an image with IPP

Non Linear normalization of an image with IPP

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.

Best regards,
Sergey

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that's correct - there are no such normalization in IPP.

Quoting Sergey KostrovHi 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'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

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.

Quoting siddyOr, 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...

Quoting Sergey Kostrov...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...

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.

Quoting Sergey Kostrov...Non Linear normalization of an image? Please take a look at:

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.

Here is an updatedexample andfrom top to bottom are

Source set of "pixels", thenLinear 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

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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