PCA - Reconstruction from a "clean" set of eigenvectors? This is a question related to the explanation here on how to reconstruct data from PCs found here:
How to reverse PCA and reconstruct original variables from several principal components?
I have two datasets (spectral imagery data) that are similar but one should contain a feature that the other doesn't (i.e. I have a "clean" image as a reference and a "experiment" image containing a feature not present in the reference).
I want to calculate the eigenvectors from the reference image and use these eigenvectors to reconstruct the experiment image. The theory being then that the difference between the real experiment image and the experiment image reconstructed using the reference eigenvectors should highlight the feature we're interested in.
The thing I don't understand is how this works in practice. 
Using this equation from How to reverse PCA and reconstruct original variables from several principal components?:
$$\boxed{\text{PCA reconstruction} = \text{PC scores} \cdot \text{Eigenvectors}^\top + \text{Mean}}$$
Can I simply drop in my "clean" eigenvectors or do I need to rescale them (or the PC scores)? Logically it would seem some rescaling would be needed somewhere but I'm not 100% clear on how.
 A: 
I want to calculate the eigenvectors from the reference image and use
  these eigenvectors to reconstruct the experiment image. The theory
  being then that the difference between the real experiment image and
  the experiment image reconstructed using the reference eigenvectors
  should highlight the feature we're interested in.

This is not likely to be as straightforward as you may be hoping, unless your new feature happens to have zero covariance with the training data. This is the only way that the new feature can give zero distortion to the PC scores, since the PC scores are the covariance between the data and the PC eigenvectors. However, you can iteratively Winsorise the most extreme differences between the new data and its reconstruction to reduce the impact of the propagation of new feature covariance into the model PC scores. 
The larger the proportion of the new data that is comprised of the new feature, the harder it will be to implement, as otherwise you end up Winsorising most of the new data and have little left to produce a reliable fit.
Also, the more the new feature correlates with the old data, the harder it will be to cleanly isolate. If new feature is like old data but one feature moves 3 pixels then PCA will see it as almost identical with a tiny residual, but other interpretation methods (e.g. database matching, feature detection) can see it as a completely different unique entity with radically different causal and scientific implications.

Can I simply drop in my "clean" eigenvectors or do I need to rescale
  them (or the PC scores)? Logically it would seem some rescaling would
  be needed somewhere but I'm not 100% clear on how.

The eigenvectors are unit vectors, so are scale free. This means that in principle there is no scaling required pre-reconstruction other than following the same pre-treatment process that was used on the training data. If scaling was used as a pre-treatment then the same mean and standard deviation values used in the training set are used for the pre-processing - these are not recalculated from the new data.
However, it should be noted that post-reconstruction there may need to be a different set of rescaling. In reconstructing the new data using an old model you can no longer assume that the covariance scales are still meaningful. For example, if working with images the original is constrained to [0,255], but there is no such constraint on the reconstruction since the new feature is not constrained to fit the covariance structure of the old data. In such cases, the final reconstruction can be rescaled to bring it back into a useable range.
A: As some of the comments already have pointed out, there is at least one wrong assumption. Regarding 

The theory being then that the difference between the real experiment image and the experiment image reconstructed using the reference eigenvectors should highlight the feature we're interested in.

The theory is not solid. The PC are a valid basis by which every point of a hyperplane can be described. If you compute the PC (by PCA) on a certain dataset, especially to reduce the dimensionality, you might end up with a minimal set that doesn't reflect the full dimensionality. You might actually want this, otherwise the dimensionality will not be reduced. If you now add a feature, which is in another dimension (e.g. not present in the data you used to evaluate the PC), then there is no general rule what will happen. 
It might not show a difference at all, it might show a different scaling of the EVs. Consider for example a reduced dimensionality: take a 2D world (a plane), if you have certain points, then you might find the best representation of a basis. If you take other 2D points in the same plane, they're represented differently in that basis. But if you add a point outside that plane -- what is its description in that basis?
