I'm trying to fit two data sets. Those contain the results of measuring the same object with two different measurement devices (x-ray vs. µct).
I did manage to reconstruct the image data and fit the orientation and offset of the stacks. It looks like this (one image from a stack of about 500 images):
The whole point of this is to compare several denoising algorithms on the x-ray data (left). It is assumed that the data from µCT (right) is close to the real signal without any noise. So, I want to compare the denoised x-ray data from each of the algorithms to the "pure" signal from µCT to see which algorithm produces the lowest RMS-error. Therefore, I need to somehow fit the grayvalues from the left part to those of the right part without manipulating the noise too much.
The gray values in the right are in the range of 0 to 100 whereas the x-ray data ranges from about 4000 to 30000. The "bubbles" are in a range of about 8000 to 11000. (those are not real bubbles but an artificial phantom with holes out of a 3D printer)
What I tried to do is (kind of) band pass those bubbles and map them to ~100 while shifting everything else towards 4 (which is the value for the background on the µCT data).
That's the code for this:
zwst = zwsr;
zwsr(zwst<=8000)=round(zwst(zwst<=6500)*4/8000);
zwsr(zwst<=11000 & zwst>8000)= round(zwst(zwst<=11000 & zwst>8000)/9500*100);
zwsr(zwst>11000)=round(zwst(zwst>11000)*4/30000);
The results look like this:
Some of those bubbles look distorted and the noise part in the background is gone completely. Is there any better way to fit those gray values while maintaining the noisy part?
EDIT: To clarify things: The µCT data is assumed to be noise free while the x-ray data is assumed to be noisy. In other words, µCT = signal while x-ray = signal + noise. To quantize the quality of my denoising methods, I want to calculate x-ray - µCT = noise.
Too long for a comment, and I believe a reasonable answer:
There is a huge subfield of image processing/ signal processing called image fusion. There is even a specific Matlab library for that using wavelets (http://uk.mathworks.com/help/wavelet/gs/image-fusion.html).
The idea behind image fusion is: given 2 images of the same thing but with very different resolution/data, how can we create a single image containing the information of both?
Stitching both images "by hand" does not give very good result generally so there are a big amount of techniques to do it mathematically. Waveletes are very common here.
Generally this techniques are widely used in medical imaging , as (like in your case) different imaging techniques give different information, and doctors want all of them together:
Example (top row: images pasted together, bottom row: image fusion techniques)
Have a look to some papers, some matlab tutorials, and probably you'll get there with the easy-to-use matlab code, without any fancy state of the art programming.
Good luck!