Histogram linearisation of images
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: 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").
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.
|
Image (input_object) |
(multichannel-)image(-array) -> Hobject: HImage(Array) ( byte ) |
| Image to be enhanced. | |
|
ImageEquHisto (output_object) |
(multichannel-)image(-array) -> Hobject * : HImage(Array) ( byte ) |
| Image with linearized gray values. | |
::scale_image, ::scale_image_max, ::illuminate
R.C. Gonzales, P. Wintz: "Digital Image Processing"; Second edition; Addison Wesley; 1987.
Image filters
