[whatshisface]

I disagree that it's always a bad idea. If you didn't have real data in those bins to begin with, then the absence of ringing was never real either. You're just choosing between interpolation strategies to fill in the data you deleted. You have to realize that whatever you do, you're making up data. One could say that you're taking out your dry erase marker and writing in your priors. For image processing, you probably don't have ringing in the scene you took a picture of, so you don't want to zero bins. In other signal processing contexts where you might actually have no signal to measure in certain bins, and sometimes you want to zero them.

[midjji]

Thats not how the frequency transform works. Its more likely to have more signal than noise in higher frequencies iif the signal is smooth and the noise white. But real relevant signals often have discontinuities.

[TeeMassive]

> If you didn't have real data in those bins to begin with, then the absence of ringing was never real either.

What is unreal data? If you have periodic data that's not aligned you're going to have a continuous signal going all the way to the high frequencies.

[whatshisface]

>What is unreal data?

The easiest example would be when an earlier step supersampled the data, and you know that nothing above the original Nyqiust could possibly reflect reality. That's one example of when you'd want to zero bins.

[TeeMassive]

Yes, but what's stopping you from using non brick wall windows and filtering nyquist frequencies before sampling? Situations where "unreal" data is present only in higher frequencies is rare, and situations where all data is absent from the higher frequencies are very rare unless you sample in a perfect context where everything is aligned with the sampling period.