@tim.holy sorry for the lack of detail for the benchmark. The code for the benchmark are in my github in the file `benchgauss.jl`

. However, thank you for pointing out the fact that I didn’t use the IIR Gaussian filter of `imfilter`

. I did it just now and effectively there is no more improvement on the computing time for my implementation of the Sugimoto’s code:

```
using BenchmarkTools, ImageFiltering, TestImages, Images, Statistics, Plots
include("CstTimeGauss.jl");
img = testimage("mandrill");
# using IIR gaussian
σ = [1; 3; 5; 10; 15; 20; 30; 50; 70; 100]
tcst = []
timf = []
for s in σ
b = @benchmark O1gaussnd(float64.($img), $s);
push!(tcst, b.times)
b = @benchmark imfilter($img, KernelFactors.IIRGaussian(($s, $s)), "reflect");
push!(timf, b.times)
end
# display mean time and standard deviation for each σ
m = hcat([mean(tcst[i]) for i in 1:length(σ)], [mean(timf[i]) for i in 1:length(σ)]) ./ 1e6
s = hcat([std(tcst[i]) for i in 1:length(σ)], [std(timf[i]) for i in 1:length(σ)]) ./ 1e6
Plots.scatter(σ, m[:, 1]; yerrors = s[:, 1], xlabel = "σ", ylabel = "time (ms)", label = "O1gaussnd", legend = :topleft)
Plots.scatter!(σ, m[:, 2]; yerrors = s[:, 2], label = "imfilter IIR")
```

But I also wanted to point out another thing: it seems that the precision of the Gaussian convolution with the code of Sugimoto et al. is better than the one from `imfilter`

. To evidence this I looked at the difference from `imfilter`

and `O1gaussnd`

and I see that this 2 give much similar results if I force `imfilter`

to use gaussian kernel of size of at least 6 \sigma:

```
# default kernel size
maximum(abs.(imfilter(img, KernelFactors.gaussian((10, 10)), "reflect") .- O1gaussnd(float64.(img), 10)))
0.05299627892037459
# kernel size of 6 σ
maximum(abs.(imfilter(img, KernelFactors.gaussian((10, 10), (61, 61)), "reflect") .- O1gaussnd(float64.(img), 10)))
0.0033663324215758017
```

But with the IIR filter I get these difference:

```
# IIR filter vs imfilter default
maximum(abs.(imfilter(img, KernelFactors.IIRGaussian((10, 10)), "reflect") .- imfilter(img, KernelFactors.gaussian((10, 10)), "reflect")))
1.025380269215756
# IIR filter vs imfilter 6 σ
maximum(abs.(imfilter(img, KernelFactors.IIRGaussian((10, 10)), "reflect") .- imfilter(img, KernelFactors.gaussian((10, 10), (61, 61)), "reflect")))
1.023126836702493
# IIR filter vs O1gaussnd
maximum(abs.(imfilter(img, KernelFactors.IIRGaussian((10, 10)), "reflect") .- O1gaussnd(float64.(img), 10)))
1.0234280940523726
```