Irvine, CA - September 27, 2026 - Digital art programs have risen in popularity as they allow artists to be much more efficient in streamlining creative processes, sharing work online, or for printing purposes. Technology can create very unique effects in art that helps artists express their vision fully with interesting digital tools.
Many of these tools are digital image processing effects. These are systems that transform pixel (the smallest unit of measurement for a digital image) data, and they are built on a pillar of STEM: mathematics!
One is called a Gaussian Blur. Credited in the name itself, this is based on the Mathematician Gauss’s Gaussian function. A Gaussian function is shaped as a bell curve, and you are likely to have seen it in statistics, where it can be used to model distribution data.
When the blur effect is applied, a function convolution happens, where two functions are combined to create a third one. Data for the original image (before the effect) is converted into a mathematical function, so the two functions being combined during this effect are of the image and the Gaussian function. These blend to create the third function, the smoothed image.
So, when this effect is applied, the function is used as a filter to blur the image. The aftermath is that a drawing looks much smoother with a gradient blur. It has an mathematically even result that is perfectly uniform and great for blending very cleanly in digital art.
Gauss’s function is also seen in another digital effect that is visually almost the opposite. This effect is Noise, random variations in color and brightness put throughout the image. Noise can be an unwanted digital image processing effect. For example, if you have ever seen a very low-quality image, there are many tiny, but random blotches of differing color or brightness that do not seem purposeful. This is a form of noise that can come from errors in the capturing phase, processing, distortion, and file compression.
However, Gaussian Noise is specifically noise spread smoothly across the entire image following the same bell curve function mentioned earlier. This function makes it so that even when small pixels deviate from the actual color, most still stay very close to the middle color. That deviation makes the effect similar to traditional art, where variations in color are present from unevenness, mixing inaccuracies (it is very hard to mix the same paint color twice!), or even leftover hue on a brush transferring over. Overall, this adds a more realistic effect on digital art that could otherwise be almost seen as sterile.
Caption: The Gaussian Noise effect applied on the character here draws the viewer’s eye because it adds texture and minuscule detail.
While subjects in STEM and art might seem very disconnected, the reality is that both areas build upon each other. Next time you see a digital drawing, appreciate the foundation of math that it is built upon.
