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I have two sets of data of given Field of view, one of them only covers a subset of the FOV of the other. I therefore want to upsample the one with the larger FOV to combine it with the other one.

So the question is: In the context of X-ray computed tomography, where the x-rays projections are backprojected to obtain a 3D reconstruction of the object, what is the best upsampling method for the purposes mentioned above?

Or to put it differently, what are the features in an x-ray image that ideally we don't want to damage too much... is it the contrast, the sharpness of the edges, etc. keep in mind, that as in many application of digital imaging, anti aliasing filters, Gaussian blurrings, etc. are performed on the images before them being processed for reconstruction purposes.

I know that each upsampling method has its merits, so considering the nature of the x-ray reconstruction methods, is there any way of deciding on one upsampling method based on the a-priori information of the object.

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  • $\begingroup$ Well it's actually filtered backprojection, right? With a differentiation stage to enhance the sharpness of edges, which would otherwise be blurred by the reconstruction? impactscan.org/slides/eanm2002/sld014.htm clear.rice.edu/elec431/projects96/DSP/filters.html So preserving edges would seem important. And the projections are not anti-alias filtered before sampling, right? So sinc interpolation is probably bad, like with any image processing, and would cause ringing at those edges? $\endgroup$ – endolith Aug 27 '13 at 16:18

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