Timeline for Why should an image be blurred using a Gaussian Kernel before downsampling?
Current License: CC BY-SA 4.0
9 events
| when toggle format | what | by | license | comment | |
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| Mar 8, 2022 at 10:00 | history | edited | Laurent Duval | CC BY-SA 4.0 |
added 9 characters in body
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| Mar 23, 2021 at 21:04 | history | edited | Laurent Duval | CC BY-SA 4.0 |
Partial correcttion (TBC)
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| Mar 23, 2021 at 16:00 | comment | added | Laurent Duval | I indeed did not stressed the computational aspect enough, I will be more precise. The separability is not so simple IMO : optimized integer approximations, or oriented/skewed 2D Gaussian filters are not generally separable. And some recursive implementations (like the Canny-Deriche version) were a bit spikier at the tip | |
| Mar 23, 2021 at 14:17 | comment | added | endolith | Gaussian kernel is separable, too, so it is computationally cheaper (You can do it on X and Y dimensions separately, and it retains its radially symmetric shape, unlike any other filter.) | |
| Oct 21, 2018 at 13:23 | vote | accept | Nagabhushan S N | ||
| Oct 20, 2018 at 17:28 | comment | added | Cris Luengo | It comes about because, in the frequency domain, you have regions where the kernel is negative. | |
| Oct 20, 2018 at 16:38 | comment | added | Laurent Duval | Let me check and further read on phase reversal, I think I never thought about it that way | |
| Oct 20, 2018 at 16:30 | comment | added | Cris Luengo | Great answer! I’d add that simple averaging causes phase reversal for certain frequency bands, and has a much worse attenuation for the frequencies it is supposed to suppress. See here for a good example of phase reversal: crisluengo.net/index.php/archives/22 | |
| Oct 20, 2018 at 12:02 | history | answered | Laurent Duval | CC BY-SA 4.0 |