The kernel consists of a rectangular array of numbers that follow a Gaussian distribution, AKA a normal distribution, or a bell curve. Each pixel in the image we wish to blur is considered independently, and its value changed depending on its own value, and those of its surroundings, based on a filter matrix called a kernel. Regardless of the image, whether color or greyscale, the basic principle of a Gaussian blur remains the same. Of course, greyscale images consist of just a single value per pixel, representing its intensity on a scale from black to white, with greys in between. Each pixel that makes up a typical digital color image has three values- its intensity in red, green and blue. Note the higher values towards the center, and growing smaller towards the outside in a bell curve shape.ĭigital images are really just lots of numbers, so we can work with them mathematically. A 2D Gaussian distribution shown in a 3D plot. Of course, we often like to dig deeper here at Hackaday, so here’s our crash course on what’s going on when you run a Gaussian blur operation. After all, it does a nice job and does indeed make things blurrier. You may have used this tool thousands of times without ever giving it greater thought. One of the most common blurs used in these fields is the Gaussian blur. Blurring is a commonly used visual effect when digitally editing photos and videos.
0 Comments
Leave a Reply. |