Non Linear Enhancement Operators

The human eye and brain are such powerful image processing tools that it is difficult to find operators that perform functions that a human being cannot do more easily. However, two non-linear operators stand out from the crowd of candidates: the extreme value operator, and local adaptive sharpening.

The extreme value operator is important because it removes subjective factors from finding regions of like pixel value in images. Humans trace "features" with little regard to the actual brightness, relying on very capable edge-detection processors in the eye and brain. As a consequence, it is difficult for a human to trace a boundary based solely on intensity. The extreme value operator creates sharp, objectively defined boundaries between image features.

Local adaptive sharpening is a detail-enhancing operator that applies a stronger enhancement to low-contrast parts of an image than it does to high-contrast parts. This operator complements the human visual system, which tends to ignore detail in low-contrast areas. After local adaptive sharpening, images may not be beautiful, but the observer is rewarded with clear detail over its entirety.

Figure 15.5 By amalgamating regions of similar pixel value, the extreme value operator sharpens the appearance of an image and creates distinct, well-defined steps in brightness that help delineate the "soft" features that define the spiral arms of this one. In this image, the neighborhood size is 5 x 5 pixels.

Figure 15.5 By amalgamating regions of similar pixel value, the extreme value operator sharpens the appearance of an image and creates distinct, well-defined steps in brightness that help delineate the "soft" features that define the spiral arms of this one. In this image, the neighborhood size is 5 x 5 pixels.

15.2.1 The Extreme Value Operator

This operator amalgamates regions of similar pixel value, making patterns such as planetary cloud belts more readily visible. Its operation is remarkably simple: within the neighborhood, it first finds the lowest and highest pixel value. It then determines whether the current pixel is closer to the minimum or to the maximum value, and assigns whichever is closer to the value of the old pixel to the new pixel. Here is pseudocode for the extreme value operator:

FOR x = 1 + xradius TO xmax - radius FOR y = 1 + yradius TO ymax - radius min = pvmax max = 0

FOR i = x - radius TO x + radius FOR j = j - radius TO j + radius

IF min > old(i,j) THEN min = old(i,j) IF max < old(i,j) THEN max = old(i,j) NEXT j NEXT i

IF old(x,y) - min <= max - old(x,y) THEN new(x,y) = min

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