Many image post-processing applications use unsharp masking (UM) as their choice of sharpening algorithm. It is one of the most ubiquitous methods of image sharpening. Unsharp masking was introduced by Schreiber [1] in 1970 for the purpose of improving the quality of wirephoto pictures for newspapers. It is based on the principle of photographic masking whereby a low-contrast positive transparency is made of the original negative. The mask is then “sandwiched” with the negative, and the amalgam used to produce the final print. The effect is an increase in sharpness.

The process of unsharp masking accentuates the high-frequency components of an image, i.e. the edge regions where there is a sharp transition in image intensity. It does this by extracting the high-frequency details from an image, and adding them to the original image. This process can be better understood by first considering a 1D signal shown in the figure below.

This is the process of what happens to the signal

1. The original signal.
2. The signal is “blurred”, by a filter which enhances the “low-frequency” components of the signal.
3. The blurred signal, ➁, is subtracted from ➀, to extract the “high-frequency” components of the signal, i.e. the “edge” signal.
4. The “edge” signal is added to the original signal ➀ to produce the sharpened signal.

In the context of digital images unsharp masking works by subtracting a blurred form of an image from the original image itself to create an “edge” image which is then used to improve the acuity of the original image. There are many different approaches to unsharp masking which use differing forms of filters. Some use a more traditional approach using the process outlines above, with the blurring actuated using a Gaussian blur, while others use specific filters which create “edge” images directly, which can be either added to, or subtracted from the original image.

[1] Schreiber, W., “Wirephoto quality improvement by unsharp masking,” Pattern Recognition, Vol.2, pp.117-121 (1970).