The "Sharpen" filter accentuates the edges in the image by finding the
edges and increasing the contrast between all adjacent pixels.
The "Unsharp Mask" filter creates the illusion of increased focus by
producing a lighter and darker line on each side of an edge. When using
this filter, the user specifies the pixel parameters to determine the
final sharpening effect. This is useful for refocusing an image which
has become blurry from repeated scanning or other interpolation and is
commonly used for high-end color correction. The on-screen results are
typically more pronounced than in high-resolution output. Applying the
Unsharp Mask filter is recommended whether your final destination is
print or online.
The "Sharpen Edges" filter sharpens only the edges of an image by finding
significant color or contrast changes and sharpening only those.
The "Sharpen Edges" filter can focus a blurry image yet still preserve its
overall tonal smoothness (i.e., results are less pixelated).
A blurry image may be improved by increasing the sharpness of its edges.
The Sharpen and Sharpen More filters allow you to focus the entire image
or selected portion thereof by increasing the contrast of all adjacent
pixels (but may increase the pixelation effect).
In contrast, the Sharpen Edges and Unsharp Mask filters sharpen only the
edges of an image by finding significant color changes and sharpening only
those. Taken to the extreme, however, these filters may render objects in
the image the appearance of being "pasted" on.
The Sharpen Edges filter can focus a blurry image yet still preserve its
overall tonal smoothness.
The Unsharp Mask filter, on the other hand, creates the illusion of
increased focus by increasing edge detail contrast by producing a lighter
and darker line on each side of an edge. This is useful for refocusing an
image which has become blurry from repeated scanning or other interpolation
and is commonly used for high-end color correction.
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Like the mean filter, the median filter considers each pixel in the image
in turn and looks at its nearby neighbors to decide whether or not it is
representative of its surroundings. Instead of simply replacing the pixel
value with the mean of neighboring pixel values, it replaces it with the
median of those values. The median is calculated by first sorting all the
pixel values from the surrounding neighborhood into numerical order and
then replacing the pixel being considered with the middle pixel value.
(If the neighborhood under consideration contains an even number of pixels,
the average of the two middle pixel values is used.) Figure 1 illustrates
an example calculation.
In general, the median filter allows a great deal of high spatial frequency
detail to pass while remaining very effective at removing noise on images
where less than half of the pixels in a smoothing neighborhood have been
effected. (As a consequence of this, median filtering can be less effective
at removing noise from images corrupted with Gaussian noise.)
One of the major problems with the median filter is that it is relatively
expensive and complex to compute. To find the median it is necessary to
sort all the values in the neighborhood into numerical order and this is
relatively slow, even with fast sorting algorithms such as quicksort. The
basic algorithm can, however,be enhanced somewhat for speed. A common
technique is to notice that when the neighborhood window is slid across
the image, many of the pixels in the window are the same from one step to
the next, and the relative ordering of these with each other will obviously
not have changed. Clever algorithms make use of this to improve performance.
Unlike the mean filter, the median filter is non-linear. This means that for
two images A and B:
median[A+B] != median(A)+median(B)
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