Image Sharpening.

Human perception is highly sensitive to edges and fine details of an image, and since they are composed primarily by high frequency components, the visual quality of an image can be enormously degraded if the high frequencies are attenuated or completed removed. In contrast, enhancing the high-frequency components of an image leads to an improvement in the visual quality. Image sharpening refers to any enhancement technique that highlights edges and fine details in an image. Image sharpening is widely used in printing and photographic industries for increasing the local contrast and sharpening the images.

In principle, image sharpening consists of adding to the original image a signal that is proportional to a high-pass filtered version of the original image. Figure (5.33) illustrates this procedure, often referred to an unsharp masking on a one-dimensional signal. As shown in Fig (5.33), the original image is first filtered by a high-pass filter that extracts the high-frequency components, and then a scaled version of the high-pass filter output is added to the original image, thus producing a sharpened image of the original. Note that the homogeneous regions of the signal, i.e., where the signal is constant, remain unchanged. The sharpening operation can be represented by

where is the original pixel value at the coordinate is the high-pass filter, is a tuning parameter greater that or equal zero, and is the sharpened pixel at the coordinate . The value taken by depends on the grade of sharpness desired. Increasing yields a more sharpened image.

If color images are used and are three-component vectors, whereas if gray-scale images are used and are single-component vectors. Thus the process described here can be applied to either gray-scale or color images, with the only difference being that vector filters have to be used in sharpening color images whereas single-component filters are used with gray-scale images.

The key point in the effective sharpening process lies in the choice of the high-pass filtering operation. Traditionally, linear filters have been used to implement the high-pass filter, however, linear techniques can lead to unacceptable results if the original image is corrupted with noise. A tradeoff between noise attenuation and edge highlighting can be obtained if a weighted median filter with appropriated weights is used. To illustrate this, consider a WM filter applied to a gray-scale image where the following filter mask is used.

	(5.8.8)

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