What to try
- Change sigma and move the inspected row through the image.
- Increase the detail amount, then set it to zero to restore the original.
What to look for: Unsharp masking adds I − blur(I) back to I. Positive and negative lobes create contrast and, at high strength, halos; plots retain overshoot that the displayed images clip.
How it works
Box blur averages a uniform neighborhood. Gaussian blur weights nearby samples more strongly and is controlled by sigma. Median filtering chooses a middle value and can remove isolated outliers without averaging every boundary. The Lab’s box and Gaussian implementations filter premultiplied color to avoid transparent-edge fringes.
Unsharp masking subtracts a softened copy to estimate detail, then adds some of that detail back. Its threshold limits weak differences. High-pass filtering shows the difference around a neutral gray. These operations can emphasize noise as well as meaningful structure.
Use it in the Lab
Choose median for isolated specks, Gaussian for smooth attenuation, and unsharp masking for controlled detail emphasis. Preview at actual output scale and use selection bounds for local treatments.
Limits & distinctions
Radius and sigma are different measures. The same numeric setting in two filter families does not imply the same blur strength.
Sources & references
Guide updated