Steve on Image Processing with MATLAB

Image processing concepts, algorithms, and MATLAB


Steve on Image Processing with MATLAB has been archived and will not be updated.

Gaussian filtering with imgaussfilt

With the R2015a release a couple of years ago, the Image Processing Toolbox added the function imgaussfilt. This function performs 2-D Gaussian filtering on images. It features a heuristic that automatically switches between a spatial-domain implementation and a frequency-domain implementation.

I wanted to check out the heuristic and see how well it works on my own computer (a 2015 MacBook Pro).

You can use imgaussfilt this way:

rgb = imread('peppers.png');
sigma = 10;
rgb_smoothed = imgaussfilt(rgb,sigma);

title('Gaussian smoothed, \sigma = 10')

With this call, imgaussfilt automatically chooses between a spatial-domain or a frequency-domain implementation. But you can also tell imgaussfilt which implementation to use.

rgb_smoothed_s = imgaussfilt(rgb,sigma,'FilterDomain','spatial');
rgb_smoothed_f = imgaussfilt(rgb,sigma,'FilterDomain','frequency');

I'll use this syntax, together with the timeit function, to get rough timing curves for the two implementation methods. The heuristic used by imgaussfilt uses a few different factors to decide, including image size, Gaussian kernel size, single or double precision, and the availability of processor-specific optimizations. For my computer, with a 2000-by-2000 image array, the cross-over point is at about $\sigma = 50$. That is, imgaussfilt uses the spatial-domain method for $\sigma < 50$, and it uses a frequency-domain method for $\sigma > 50$.

Let's check that out with a set of $\sigma$ values ranging from 5 to 200.

I = rand(2000,2000);
ww = 5:5:200;
for k = 1:length(ww)
    t_s(k) = timeit(@() imgaussfilt(I,ww(k),'FilterDomain','spatial'));

for k = 1:length(ww)
    t_f(k) = timeit(@() imgaussfilt(I,ww(k),'FilterDomain','frequency'));

hold on
hold off
ylabel('time (s)')
title('imgaussfilt - 2000x2000 image')

As you can see, the cross-over point of the timing curves is about the same as the threshold used by imgaussfilt. As computing hardware and optimized computation libraries evolve over time, though, the ideal cross-over point might change. The Image Processing Toolbox team will need to check this every few years and adjust the heuristic as needed.

Published with MATLAB® R2016b

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