The Enhancement Filter

2.5.3 The Enhancement filter:

The enhancement filters are:
1. Laplacian type.
2. Difference filter.
These filters will tend to bring out, or enhance details in the image.
Example of convolution masks for the Laplacian-type filters are:




The difference filters will enhance details in the direction specific to the mask selected. There are four different filter convolution masks, corresponding to lines in the vertical, horizontal and two diagonal directions.













Laplacian Edge Enhancement
Description: The Laplacian edge enhancement operation extracts all of the edges in an image, regardless of direction. The resulting image appears as an Omni-directional outline of the objects in the original image. Constant brightness regions become black, while changing brightness regions become highlighted.

Application: The Laplacian edge enhancement operation is used to produce the edges of the objects in an image. The edges are any sharp brightness transitions rising from black to white or falling from white to black. this operation is Omni-directional, which means that all black-to-white and white-to-black edge transitions are highlighted, regardless of their directions in the image.

Implementation: The Laplacian edge enhancement operation can produce results that are less than 0 or greater than 255. In these underflow and overflow cases, the resulting values are forced to 0 or 255, whichever is closest.
The mask coefficients add to 0. A large coefficient generally appears in the center of the mask, surrounded by smaller positive and negative coefficients. Three common Laplacian edge enhancement masks are:
-1 -1 -1 0 -1 0 1 -2 1
-1 8 -1 -1 4 -1 -2 4 -2
-1 -1 -1 0 -1 0 1 -2 1

Mask 1 Mask 2 Mask 3











Shift and Difference Edge Enhancement
Description: Shift and Difference operation extracts vertical, horizontal, or diagonal edges in an image. Each pixel is subtracted from its adjacent neighboring pixel in the chosen direction. The resulting image appears as a directional outline of the objects in the original image: Constant brightness regions become black, while changing brightness regions become highlighted.

Application: The Shift and Difference operation is used to produce the edges of the objects in an image. The edges are any sharp brightness transitions rising from black to white. This operation is directional, which means that black-to-white edge transitions are highlighted only in single direction of travel across the image. The shift and difference operation is the most computationally efficient of all edge enhancement operations, but it tend to accentuate noise in an image.

Implementation: You can implement this operation geometrically translating the original image by one pixel and subtracting the result from the original. Implementing the operation as a group process can often be a more efficient approach. The mask coefficients add to 0. the center coefficient is 1 and another coefficient is -1. depending on where the -1 is, a horizontal, vertical, or diagonal edge enhancement occurs.

The Shift and Difference operation can produce results that are less than 0. This underflow condition occurs when a bright pixel is subtracted from a dark pixel. In these underflow cases, the resulting value is forced to 0. the net effect is that edges going from black to white are brightly highlighted. Edges going from white to black are not. Because the bright value is subtracted from the dark value, so the underflow condition yields a black result.

The three common shift and difference masks are:



Vertical Horizontal Diagonal