I don't understand how the Gaussian can introduce ringing artifacts. It doesn't have any negative lobes.
If the scene you are sampling contains arbitrarily high frequencies, there's no way to avoid aliasing, yes. But surely ringing per se is always a product of the reconstruction filter. (Fourier transforms etc. are a different matter.)
> I don’t understand how the Gaussian can introduce ringing artifacts. It doesn’t have any negative lobes.
I don’t think Gaussian can. (I hope it didnt seem like suggested that) But a box filter can alias, and it doesn’t have any negative lobes. So can Lanczos & Mitchell, but to a much lesser degree than a box filter.
It’s a fun exercise to plot sin(1/x) and try to get rid of all visible aliasing. It can be surprising to see aliasing when you take 100,000 samples per pixel.
> if the scene you are sampling contains arbitrary high frequencies, there’s no way to avoid aliasing.
Right, yes exactly. Though Gaussian is pretty dang good, the best I’ve found personally. A lot of samples & a Gaussian that is just a tiny bit soft, and I can usually remove any signs of aliasing.
Gaussian always looks fuzzy, even if you are careful about it.
Your personal preference might be for fuzzy-edged images, but sharper ones will look better to almost all observers, including both professional photographers and laypeople. It depends a lot on the precise details of how you handle the sharpening / resampling filtering; many available tools do a crappy job.
In general the laypeople prefer images to be sharper than you would expect and don’t care much about artifacts (at least, in my experience asking people off the street to pick between two choices of images with different amounts of sharpening), whereas image experts tend to be a bit more conservative if there are noticeable artifacts, especially aliasing, etc.
If you are printing photos on paper, I recommend sharpening beyond your initial inclination, and then sharpening some more, because the printing process tends to bring some fuzzies back.
Note that the human visual system inherently introduces ringing artifacts even if they aren’t there in the original. There’s no inherent problem with amplifying these slightly; the visual effect if you do it subtly will be to imply more contrast than is actually available, rather than obviously appearing like an artifact.
Most types of images will look better if you stretch your available contrast to the extent you can. If you allow some ringing artifacts, you can get away with less real contrast for details, giving you more room to add large-scale contrast between shapes or regions of your picture.
> Gaussian always looks fuzzy, even if you are careful about it.
Yeah, I agree, and it even looks fuzzy to me, I’ve just grown accustomed to it, and I rationalize / theorize that I’m not losing detail even if it looks soft.
What I really want is to not be able to see any sign of pixels at all; to be completely unable to tell how large a pixel is, or tell whether the image is high res and soft or sharper but low res.
If the scene you are sampling contains arbitrarily high frequencies, there's no way to avoid aliasing, yes. But surely ringing per se is always a product of the reconstruction filter. (Fourier transforms etc. are a different matter.)