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.

----------------

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)

-----------------