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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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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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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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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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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", 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
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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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