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Comment by salty_biscuits | Hacker News Reader

I might be wrong (so happy to be corrected by a native speaker) but I liked when I heard that the German word for convolution is "faltung" which translates to "folding". This is a much nicer word to metaphorically understand the operation. Maybe it still confuses the hell out of German undergrads though?

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The etymology is the same in English.

To convolve : to roll together : writhe.

Convolution : a form or shape that is folded in curved or tortuous windings.

That confuses the hell out of me.

I first encountered it as a young gamedev hobbyist, doing image post-processing in OpenGL.

One kind of kernel makes images blurry, one (attempts to) make them sharp.

It doesn't bring folding to mind at all. And in FP, "fold" means "reduce".

How much of dyscalculia is just one person thinking an analogy is completely obvious and the other person thinking it makes no sense?

Probably due to the really idiosyncratic way I think about things and from the post it is not at all clear what I mean, sorry for the imprecision! I think of cross correlation of 1D signals with a kernel as sliding one of the functions over the other [0]. In my head I imagine drawing the signal and kernel next to each other one a big piece of paper and literally folding it over and sliding it back over the signal to intuit the convolution (with an imaginary see through piece of paper). I really don't think that was obvious from what I posted, so definitely not dyscalculia!

https://commons.wikimedia.org/wiki/File:Cross_correlation_an...

> One kind of kernel makes images blurry, one (attempts to) make them sharp.

Any kernel with positive values will blur the image, and the effect will be quite similar to Gaussian blur (by the Central Limit Theorem).

It's kernels with positive and negative coefficients that may "enhance" or "sharpen" the image.

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