Spatial domain techniques

These techniques are based on gray level mappings, where the type of mapping used depends on the criterion chosen for enhancement. As an eg. consider the problem of enhancing the contrast of an image. Let r and s denote any gray level in the original and enhanced image respectively. Suppose that for every pixel with level r in original image we create a pixel in the enhanced image with level . If has the form as shown

The effect of this transformation will be to produce an image of higher contrast than the original by darkening the levels below a value m and brightening the levels above m in the original pixel spectrum. The technique is refered to as contrast stretching. The values of r below m are compressed by the transformation function into a narrow range of S towards the dark end of the spectrum; the opposite effect takes place for values of r above m .

In the limiting case shown in figure, produces a 2-level (binary) image. This is also referred to as image thresholding. Many powerful enhancement processing techniques can be formulated in the spatial domain of an image.

Note: It is to be noted that there is no general theory of image enhancement. When an image is processed for visual interpolation, the observer is the ultimate judge of how well a particular method works. Visual evaluation of image quality is a subjective process thus making the definition of a "good image" an elusive standard by which to compare algorithm performance.

When the problem is one of processing images for machine perception, the evaluation task is easier. For eg. if we take the problem of character recognition by a machine the best image processing method would be the one that yields the best machine recognition result.. In general, even where there is a clear cut criterion of performance imposed on a problem there is usually a certain amount of trial and error before one selects a particular image processing approach.