equ_histo__ ( Image : ImageEquHisto : : )

Histogram linearisation of images

The operator equ_histo__ enhances the contrast. The starting point is the histogram of the input images. The following simple gray value transformation f(g) is carried out: f(g) = 255 * SUMx=0..g { h(x) } h(x) describes the relative frequency of the occurrence of the gray value x. This transformation linearises the cumulative histogram. Maxima in the original histogram are "spreaded" and thus the contrast in image regions with these frequently occuring gray values is increased. Supposedly homogenous regions receive more easily visible structures. On the other hand, of course, the noise in the image increases correspondlingly. Minima in the original histogram are dually "compressed". The transformed histogram contains gaps, but the remaining gray values used occur approximately at the same frequency ("histogram equalization").


Attention

The operator equ_histo__ primarily serves for optical processing of images for a human viewer. For example, the (local) contrast spreading can lead to a detection of fictitious edges.


Parameters

Image (input_object)
image(-array) -> object : byte
Image to be enhanced.

ImageEquHisto (output_object)
image(-array) -> object : byte
Image with linearized gray values.


Possible Successors

disp_image


Alternatives

scale__, scale_max, illuminate__


See also

histo__


References

R.C. Gonzales, P. Wintz: "Digital Image Processing"; Second edition; Addison Wesley; 1987.



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