| Image Processing
Sub-System
Image Processing
In the context of the SMART-PIV project, Digital
Image Processing operations have been studied in order to understand
if improvements in Piv algorithm calculations, to be defined successively,
can be reached.
Digital Image Processing encompasses a wide variety
of techniques and mathematical tools. In principle all these operations
and tools can be divided in two great classes: Image Pre-processing
and Image Analysis. The main difference is that Image Pre-processing
operations give an image as output, having accepted an image as
input, instead of Image Analysis ones that produce data or reports
of some type, in some cases these can be images too but not necessarily.
In the SMART-PIV project, the operations to be studied
and, once they have proved to work properly, implemented have to
be chosen looking at all the problems that can be encountered: reflections,
noises of different kind and no-flow areas – whose presence
influences the Piv results in the neighbouring zones.
Image Pre-processing tested operations
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- Mean weighted square filter
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- Mean weighted cross filter
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- K-nearest neighbor filter
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- Inverse gradient based filter
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- Maximum homogeneity filter (mean based)
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- Maximum homogeneity filter
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- Adaptive filter (threshold and mean)
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- Laplacian plus Image filter (unsharp masking)
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