Comparison with deconvolution 

For image processing, deconvolution is the process of approximately inverting the process that caused an image to be blurred. Specifically, unsharp masking is a simple linear image operation—a convolution by a kernel that is the Dirac delta minus a gaussian blur kernel. Deconvolution, on the other hand, is generally considered an ill-posed inverse problem that is best solved by nonlinear approaches. While unsharp masking increases the apparent sharpness of an image in ignorance of the manner in which the image was acquired, deconvolution increases the apparent sharpness of an image, but is based on information describing some of the likely origins of the distortions of the light path used in capturing the image; it may therefore sometimes be preferred, where the cost in preparation time and per-image computation time are offset by the increase in image clarity.

With deconvolution, "lost" image detail may be approximately recovered, although it generally is impossible to verify that any recovered detail is accurate. Statistically, some level of correspondence between the sharpened images and the actual scenes being imaged can be attained. If the scenes to be captured in the future are similar enough to validated image scenes, then one can assess the degree to which recovered detail may be accurate. The improvement to image quality is often attractive, since the same validation issues are present even for un-enhanced images.

For deconvolution to be effective, all variables in the image scene and capturing device need to be modeled, including aperture, focal length, distance to subject, lens, and media refractive indices and geometries. Applying deconvolution successfully to general-purpose camera images is usually not feasible, because the geometries of the scene are not set. However, deconvolution is applied in reality to microscopy and astronomical imaging, where the value of gained sharpness is high, imaging devices and the relative subject positions are both well defined, and optimization of the imaging devices to improve sharpness physically would cost significantly more. In cases where a stable, well-defined aberration is present, such as the lens defect in early Hubble Space Telescope images, deconvolution is an especially effective technique.

Implementation 

In the example below, the image is convolved with the following sharpening filter:

Sharpen filter

This matrix is obtained using the equation shown above under #Digital unsharp masking, using a uniform kernel with 5 pixels for the "blurred" image, and 5 for the "amount" multiplier:

The sharpening effect can be controlled by varying the multiplier. The value of 5 was chosen here to yield a kernel with integer values, but this is not a requirement for the operation.

The second image has been sharpened twice as much as the first.

See also 
 Edge enhancement
 Cornsweet illusion

References

General references
 Sharpening With a Stiletto, Dan Margulis, February, 1998
 Life on the Edge, Dan Margulis, January, 2005

External links
 Excel spreadsheet that calculates an Unsharp Mask
 Interactive Example of Unsharp Mask
 PhotoKit Sharpener User Guide
 Sharpening 101, mirror of by thom, Aug 1, 2003
 The Unsharp Mask: Analog Photoshop, Sample of unsharp masking in the darkroom, before digital

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