Nice series on aliasing!

I think the key here is to understand that the two low-pass filters (interpolation filter of step 1 and blurring filter of step 2) have the same shape but different width. When multiplied in the Fourier domain, they lead to a single low-pass filter that is the narrower of the two (wider in the spatial domain). Thus, if filter #2 is wider, you use that one, otherwise you use #1. This is why you can simply make the interpolation filter wider when down-sampling, and don’t change anything when up-sampling.

]]>The reason is that you actually acquire the image in Fourier space; frequency sampling density corresponds to field-of-view. Insufficient sampling gives an image that’s too small, leading too aliasing.

]]>Don’t sell your students short. If they understand the concept of aliasing, the vocabulary will be no problem.

]]>So, some people might like the term under-sampling as a better introductory alternative term. Then you might explain that it is also called ~aliasing~ because one frequency appears to be another.

The zone plate example is great!

– TK

]]>lettersAndHoles = imclearborder(~initialBinaryImage);

holesOnly = imclearborder(imfill(lettersAndHoles, [1,1]));

filledLetters = imfill(initialBinaryImage, ‘holes’) & ~holesOnly;