equ_histo_image — Histogram linearization of images
equ_histo_image(Image : ImageEquHisto : : )
The operator equ_histo_image enhances the contrast. The
starting point is the histogram of the input images. The following
simple gray value transformation f(g) is carried out for byte
images:
h(x) describes the relative frequency of the occurrence of the
gray value x. For uint2 images, the only difference is that the
value 255 is replaced with a different maximum value. The maximum
value is computed from the number of significant bits stored with
the input image, provided that this value is set. If not, the value
of the system parameter 'int2_bits' is used (see
set_system), if this value is set (i.e., different from -1).
If none of the two values is set, the number of significant bits is
set to 16.
This transformation linearizes the cumulative histogram. Maxima in the original histogram are “spreaded” and thus the contrast in image regions with these frequently occurring gray values is increased. Supposedly homogeneous regions receive more easily visible structures. On the other hand, of course, the noise in the image increases correspondingly. 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”).
The operator equ_histo_image 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.
Note that filter operators may return unexpected results if an image with a reduced domain is used as input. Please refer to the chapter Filters.
Image (input_object) (multichannel-)image(-array) → object (byte / uint2)
Image to be enhanced.
ImageEquHisto (output_object) (multichannel-)image(-array) → object (byte / uint2)
Image with linearized gray values.
scale_image,
scale_image_max,
illuminate
R.C. Gonzales, P. Wintz: “Digital Image Processing”; Second edition; Addison Wesley; 1987.
Foundation