Talk:Gaussian filter

message: Can the introductory sentences be simplified? It looks like it's written for people who already understand specialized terminology. Chris (talk) 15:56, 9 August 2013 (UTC)
replies: text: No, but they are related. A simple Gaussian filter filters a signal (1D), Gaussian blur is 2D. --84.150.198.254 22:12, 21 October 2006 (UTC)
replies: text: No, that's an old discussion. A lot of the material in the filter article applies to the blurring article, plus there's duplicated stuff. Both articles really refer to Gaussian filters (except one current covers the 1D and the other the 2D), so I see no reason why they can't be merged. -Roger (talk) 21:20, 31 March 2009 (UTC)
thread_title: Gaussian blur

message: It looks to me like the gaussian filter, as defined here, is non-causal.
Is this correct? 
If so, how does one approximate a gaussian filter in the "real" world?
Thanks, 
Mherndon 16:23, 2 July 2007 (UTC)
replies: text: A binomial filter (a FIR filter with binomial coefficients) is an approximation to a gaussian filter. --89.59.137.109 00:16, 3 July 2007 (UTC)
thread_title:  non-causal? 

message: I edited the digital implementation section, but left the tag on because I'm not sure whether the math is correct or not.  The first few paragraphs seem OK as far as I know (not sure) but the units seem wrong for the cut-off frequency f/sigma.  f is already in samples per second, and sigma would be in seconds, I think. ☺Coppertwig (talk) 22:54, 17 April 2010 (UTC)
replies: text: The article may be measuring sd in samples rather than time units, but I agree, the cleanup template should stay: the article is far from clear to technically minded readers, let alone a general reader.  It also fails to give its source. SpinningSpark 10:23, 18 April 2010 (UTC)
thread_title:  Digital implementation section 

message: The last paragraph under the Definition subheading suddenly starts talking about image and pixel from the second line. I think this is out of context there. The 2-D Gaussian filter should be described in a generic mathematical context rather than particular application (to graphics/image processing).  — Preceding unsigned comment added by Srays (talk • contribs) 09:27, 11 January 2011 (UTC)
thread_title:  Sudden jump to digital image processing 

message: The article says "Gaussian filters have the properties of having no overshoot to a step function input while minimizing the rise and fall time.". All filters with non-negative (or non-positvie) impulses have no overshoot. The fastest filter without overshoot is the dirac delta (it is instantaneous). It should probably be the fastest filter subjected to some constraint.LarsPM (talk) 16:13, 16 October 2013 (UTC)
thread_title:  Minimizing the rise and fall time? 

message: A function is given in terms of the standard deviation in time-domain, and then a frequency response function is given.  At first, because of poor wording, the frequency response function appears to be the frequency response of the same preceding time-domain function, but upon looking closer I see that the standard deviation for the frequency-domain function is in terms of the standard deviation in frequency units.  Either "standard deviation" should mean the same in both functions, or language should be introduced that clarifies that the two functions use a different meaning for standard deviation (sigma and sigma-f are the inverse of each other -- sigma-f = 1/sigma).  — Preceding unsigned comment added by 2601:643:201:6351:E1CA:D283:CDEB:13A7 (talk) 19:32, 27 November 2015 (UTC)
thread_title:  Second frequency response function description is badly worded 

message: The Definition section mentions a but does not explain what it is. It is not obvious to a non-mathematician.  — Preceding unsigned comment added by 184.70.193.174 (talk) 18:54, 26 February 2019 (UTC)
thread_title:  What is <math>a</math>? 

message: The formula :f_c = \sqrt{2\ln(c)}\cdot\sigma_f  appears to be incorrect. My derivation, supported by numerical calculations indicates that the proper formula is :f_c = \sqrt{ln(c)}\cdot\sigma_f = \frac{\sqrt{ln(c)}}{2\pi\sigma}  Also I found a reference that suggests the proper (rearranged) formula, but regrettably with an improper derivation. I would appreciate help finding other references.
https://www.gaussianwaves.com/2012/05/derivation-of-expression-for-a-gaussian-filter-with-3-db-bandwidth/
--Gnobe (talk) 20:32, 2 September 2019 (UTC)
thread_title:  Incorrect formula for frequency cutoff with arbitrary attenuation 

message: re this edit, what do you mean by "infinite support"?  Is that a typo? SpinningSpark 15:20, 19 August 2021 (UTC)
replies: text: I've tried to clarify it now: . fgnievinski (talk) 16:09, 19 August 2021 (UTC)
thread_title:  Infinite support 

message: The introduction says, "It is considered the ideal time domain filter, just as the sinc is the ideal frequency domain filter.[1]" I'm having trouble understanding this: the equivalent of a sinc() in frequency would be a rect() in time domain. A Gaussian in time is a Gaussian in frequency. Sinc() is useful if you care about having absolute cutoff in frequency and are willing to have big tails in the time domain. Gaussian gives the tightest combination of tails in time and frequency domain, e.g., uncertainty principle. I'd like to modify this section to reflect that. David (talk) 00:14, 17 June 2022 (UTC)
thread_title:  Rephrase purpose in introduction?