‘Gaussian’ blur explained

Añadido: 2026-05-17

[00:00:00]If I put 100 people in a room and measured all their heights and then I plot all their heights on a graph, I probably get something that looks like this. The more common heights are in the middle with the less common ones trailing off outwardly from the center in a smooth curve. This bell curve we get is called a normal distribution or a Gaussian distribution after Carl Frederick Gaus, a mathematician and astronomer who used it to track an asteroid in the 1800s and became super famous for it. This graph distribution got named after Gaus and we see it all over the place in human nature. So what does this have to do with graphic design? Well, imagine I'm in a photo editing tool like Photoshop and I want to blur an image. Our image processor might blur the color value of each pixel by taking the surrounding pixels and then finding the average color of those pixels and then assigning that value to the pixel in the center. The greater the radius of the pixels we sample, the blurrier the image. We can do that in Photoshop with a box blur. A box blur averages all the pixels inside a square evenly. Well, it turns out that if we give more weight to the value of the central pixel and less weight to the edge of the radius using a bell curve to define the strength of that sample, this actually gives a smoother looking blur.

[00:01:01]This is why a Gaussian blur is more popular in Photoshop. It just looks nicer. So, the next time you apply a Gaussian blur in Photoshop, spread a thought for our mate Carl.