Non-local means

Imagine you have a photo that you want to make prettier. But there might be some unwanted things in the photo. For example, the photo might have a little bit of noise or graininess. Non-local means is like a tool that helps clean up the photo by removing the unwanted noise or graininess.

Here is how it works:

First, we need to look at "neighbors". Neighbors are like your friends who live near your house. In the world of image processing, neighbors are pixels that are near each other.

Next, the non-local means algorithm looks at a bunch of these neighbors, not just the ones right next to the pixel. Think of it like you're trying to find the best friend for a person not just in their own class, but across all the classes in the school. This is called searching the "patch".

So, the non-local means algorithm looks at a lot of neighbors and compares them to find the ones that are most similar. It does this by calculating what is called the "distance" between the pixels. Think of a distance like the space between your house and your friend's house. The computer calculates this distance for every pair of pixels in the photo.

Once it knows which neighbors are most similar, non-local means replaces the noisy pixels with the average values of the similar neighbors. Think of this like how you might copy your friend's homework when you didn't understand something - you take the average of several friends' homework to make sure you get the right answer.

This process makes the photo look cleaner and nicer. Non-local means is actually used in many photo editing software programs like Adobe Photoshop to make your photos look better.