K L

hLA(m, n)(i)=^ J25(f(m + 1, n + k)-i), i — 0,1, ...P - 1

Hla(m, n)(^)^(2K + 1)1 (2L + 1) £ hLA(m n)(i), j — 0,1, ...P - 1 g(m, n) — (P - 1) • HLA(m, n)(f (m, n))

where hLA(m, n)(i) is the local-area histogram, HLA(m, n)(j) is the local-area cumulative histogram, and g(m, n) is the output image. Figure 8 shows the output image obtained by enhancing the image in Fig.2a with local-area histogram equalization using K — L — 15 or a 31 x 31 kernel size.

Local-area histogram equalization is a computationally intensive enhancement technique. The computational complexity of the algorithm goes up as the square of the size of the kernel. It should be noted that since the transformation that is applied to the image depends on the local neighborhood only, each pixel is transformed in a unique way. This results in higher visibility for hidden details spanning very few pixels in relation to the size of the full image. A significant limitation of this method is that the mapping between the input and output images is nonlinear and highly nonmonotonic. This means that it is inappropriate to make quantitative measurements of pixel intensity on the output image, as the same gray level may be transformed one way in one part of the image and a completely different way in another part.

FIGURE 8 Output image obtained when local-area histogram equalization was applied to the image in Fig. 2a. Note that the local-area histogram equalization produces very high-contrast images, emphasizing detail that may otherwise be imperceptible. This type of enhancement is computationally very intensive and it may be useful only for discovery purposes to determine if any evidence of a feature exists.

FIGURE 8 Output image obtained when local-area histogram equalization was applied to the image in Fig. 2a. Note that the local-area histogram equalization produces very high-contrast images, emphasizing detail that may otherwise be imperceptible. This type of enhancement is computationally very intensive and it may be useful only for discovery purposes to determine if any evidence of a feature exists.

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