Here is a good illustration of this "best so far" interpolatory EWA filter I found (which, once again, is not very good).
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. Now, try this:
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convert sl.png -define filter:blur=.7071067811865475 -define filter:c=.49218815 -define filter:b=2.08817747090797 -filter Cubic -distort Resize 3000% c_optimal_3000percent.png
Neat, eh? Is it clear why I say that it's fine for mild, but not for drastic, upsampling?
Now, before you go "this is complete garbage", compare with
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convert sl.png -filter Triangle -distort Resize 3000% Triangle.png
If you want to compare with non-interpolatory EWA filters:
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convert sl.png -define filter:filter=Jinc -define filter:window=Jinc -define filter:blur=0.88451002338585141 -define filter:lobes=4 -distort Resize 3000% JincJinc4blur0p88451002338585141.png
convert sl.png -define filter:filter=Jinc -define filter:window=Jinc -define filter:blur=0.88549061701764 -define filter:lobes=3 -distort Resize 3000% JincJinc3blur0p88549061701764.png
convert sl.png -filter LanczosSharp -distort Resize 3000% LanczosSharp.png
convert sl.png -filter Robidoux -distort Resize 3000% Robidoux.png
convert sl.png -define filter:c=.3689927438004929 -filter Cubic -distort Resize 3000% RobidouxSharp.png
And this is good old orthogonal (Sinc-windowed Sinc) Lanczos 3:
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convert sl.png -filter Lanczos -resize 3000% orthogonalLanczos3.png
If you compare orthogonalLanczos to, say, RobidouxSharp, you'll get an idea of why I like the latter despite the fact that it is not derived with any consideration of frequency response whatsoever, and why I don't think that orthogonalLanczos is the be all and end all. (A look at the LanczosSharp result will also tell you why I, and others, like it so much.)