This function can at least partially repair a picture that suffers from blurring. It is a good idea to use this function after resizing a picture.
To reach this function, use Adjust | Sharpen… [Ctrl+5] to sharpen pictures. The most important option in this window is the sharpening type.
- Simple sharpening is a quick way to sharpen fine details in certain situations such as after you have shrunk a picture. For this type, you can choose an effect strength and whether or not to use the Brightness method.
- Unsharp mask has its roots in film-camera technology. It sharpens only highly visible edges and borders. For this type, you can choose an effect strength, a radius, and a threshold, and toggle the Brightness method.
- Gaussian sharpening is a method for removing Gaussian blur. For this method, you can choose an effect strength, a radius, and noise suppression.
- Overall sharpening gets rid of overall blur in a picture. For this method, you can choose an effect strength, a radius, and noise suppression.
- Soft sharpening sharpens a picture’s fine details while suppressing rough structures, making it useful for sharpening e.g. portraits. You can adjust the effect strength.
The Brightness Method setting means that the filter will only be applied to the picture’s Lightness element within the HSL color model. This prevents the shifts in color that can otherwise happen around borders when you are sharpening strongly.
Differences Among Sharpening Methods
The various sharpening methods have considerably varying effects on pictures. While the Unsharp mask sharpens mainly highly visible edges in a picture, the other methods always sharpen the whole picture, thus emphasizing all details. In practice this means that in low-quality photos – whether they suffer from noise or excessive compression – the Unsharp Mask is the most advantageous of the methods, since the remaining ones would overly emphasize the less attractive parts of the picture.
Use the unsharp mask method to eliminate unsharpness that arose while taking the picture, scanning, etc. This method is very appropriate for photographs because it uses the details of the picture itself for its decisions. The basic idea behind this method is simple. A blurred copy of the original picture is created, and then that copy is “subtracted” from the original. The new copy that results from this has highlighted edges. This new copy is then “added into” the original. The Radius sets how much the mask is blurred, so its size is very important. Too high a value will cause oversharpening, which will show up as bright (or even shining) contours around any edges in the picture. The Threshold determines how different two brightness values should be before they are treated as an edge. A value of 0 means that the effect will be used on all pixels in the picture. If the effect overly emphasizes noise, then we recommend that you experiment with values in the range from 2 to 20.
The Gaussian sharpening and Overall sharpening methods are designed to remove concrete types of blurring using what is called a convolution matrix. The Overall type can rescue pictures damaged by unsharpness during picture-taking itself. Gaussian sharpening helps pictures that became blurred during processing – e.g. during shrinking. Radius sets how much of each pixel’s surroundings will be included in calculations. A larger radius will be subjectively perceived as a much stronger sharpening effect. The Noise suppression can prevent oversharpening without preventing sufficient sharpening.
When you shrink a picture using supersampling, this causes overall blurring, not Gaussian, but this is somewhat of an exception. Blurring that arises during picture-taking tends to lie on the border between Gaussian and overall blurring.