Unsharp masking is an image sharpening technique, often available in digital image processing
software.
The "unsharp" of the name derives from the fact that the technique uses a blurred, or
"unsharp", positive image to create a mask of the original image.
The unsharped mask is then combined with the negative image, creating an image that is
less blurry than the original.
The resulting image, although clearer, may be a less accurate representation of the image's
subject.
In the context of signal processing, an unsharp mask is generally a linear or nonlinear filter
that amplifies the high-frequency components of a signal.
Photographic unsharp masking
The technique was first used in Germany during the 1930s as a way of increasing the acutance,
or apparent resolution, of photographic images.
For the photographic process, a large-format glass plate negative is contact-copied onto
a low contrast film or plate to create a positive image.
However, the positive copy is made with the copy material in contact with the back of
the original, rather than emulsion-to-emulsion, so it is blurred.
After processing this blurred positive is replaced in contact with the back of the original
negative.
When light is passed through both negative and in-register positive, the positive partially
cancels some of the information in the negative.
Because the positive has been blurred intentionally, only the low frequency information is cancelled.
In addition, the mask effectively reduces the dynamic range of the original negative.
Thus, if the resulting enlarged image is recorded on contrasty photographic paper, the partial
cancellation emphasizes the high spatial frequency information in the original, without loss
of highlight or shadow detail.
The resulting print appears more acute than one made without the unsharp mask: its acutance
is increased.
In the photographic procedure, the amount of blurring can be controlled by changing
the "softness" or "hardness" of the light source used for the initial unsharp mask exposure,
while the strength of the effect can be controlled by changing the contrast and density of the
unsharp mask.
For traditional photography, unsharp masking is usually used on monochrome materials; special
panchromatic soft-working black and white films have been available for masking photographic
colour transparencies.
This has been especially useful to control the density range of a transparency intended
for photomechanical reproduction.
Digital unsharp masking
The same differencing principle is used in the unsharp-masking tool in many digital-imaging
software packages, such as Adobe Photoshop, PicMonkey, and GIMP.
The software applies a Gaussian blur to a copy of the original image and then compares
it to the original.
If the difference is greater than a user-specified threshold setting, the images are subtracted.
The threshold control constrains sharpening to image elements that differ from each other
above a certain size threshold, so that sharpening of small image details, such as photographic
grain, can be suppressed.
Digital unsharp masking is a flexible and powerful way to increase sharpness, especially
in scanned images.
Unfortunately, it may create unwanted conspicuous edge effects, or increase image noise.
However, these effects can be used creatively, especially if a single channel of an RGB or
Lab image is sharpened.
Undesired effects can be reduced by using a mask—particularly one created by edge
detection—to only apply sharpening to desired regions, sometimes termed "smart sharpen".
Typically three settings control digital unsharp masking:
Amount is listed as a percentage, and controls the magnitude of each overshoot.
This can also be thought of as how much contrast is added at the edges.
It does not affect the width of the edge rims.
Radius affects the size of the edges to be enhanced or how wide the edge rims become,
so a smaller radius enhances smaller-scale detail.
Higher Radius values can cause halos at the edges, a detectable faint light rim around
objects.
Fine detail needs a smaller Radius.
Radius and Amount interact; reducing one allows more of the other.
Threshold controls the minimum brightness change that will be sharpened or how far apart
adjacent tonal values have to be before the filter does anything.
This lack of action is important to prevent smooth areas from becoming speckled.
The threshold setting can be used to sharpen more-pronounced edges, while leaving subtler
edges untouched.
Low values should sharpen more because fewer areas are excluded.
Higher threshold values exclude areas of lower contrast.
Various recommendations exist as to good starting values for these parameters, and the meaning
may differ between implementations.
Generally a radius of 0.5 to 2 pixels and an amount of 50–150% is a reasonable start.
It is also possible to implement USM manually, by creating a separate layer to act as the
mask; this can be used to help understand how USM works, or for fine customization.
Local contrast enhancement Unsharp masking may also be used with a large
radius and a small amount which yields increased local contrast, a technique termed local contrast
enhancement.
USM can increase either sharpness or contrast because these are both forms of increasing
differences between values, increasing slope—sharpness referring to very small-scale differences,
and contrast referring to larger-scale differences.
More powerful techniques for improving tonality are referred to as tone mapping.
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 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 the imaging devices would cost a great deal more to optimize to
improve sharpness physically.
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.
See also Edge enhancement
Cornsweet illusion References
External links Interactive Example of Unsharp Mask
PhotoKit Sharpener User Guide Sharpening 101, by thom, Aug 1, 2003
Sharpening in Photoshop – Part I, Ron Bigelow – detailed discussion of sharpening, beginning
with USM The Unsharp Mask: Analog Photoshop, Sample
of unsharp masking in the darkroom, before digital