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Adaptive histogram equalization (AHE) is a computer image processing technique used to improve contrast in images. It differs from ordinary histogram equalization in the respect that the adaptive method computes several histograms, each corresponding to a distinct section of the image, and uses them to redistribute the lightness values of the image.
Histogram equalization will work the best when applied to images with much higher color depth than palette size, like continuous data or 16-bit gray-scale images. There are two ways to think about and implement histogram equalization, either as image change or as palette change.
An adaptive equalizer is an equalizer that automatically adapts to time-varying properties of the communication channel. [1] It is frequently used with coherent modulations such as phase-shift keying , mitigating the effects of multipath propagation and Doppler spreading .
Many of the techniques of digital image processing, or digital picture processing as it often was called, were developed in the 1960s, at Bell Laboratories, the Jet Propulsion Laboratory, Massachusetts Institute of Technology, University of Maryland, and a few other research facilities, with application to satellite imagery, wire-photo standards conversion, medical imaging, videophone ...
An example of histogram matching In image processing , histogram matching or histogram specification is the transformation of an image so that its histogram matches a specified histogram. [ 1 ] The well-known histogram equalization method is a special case in which the specified histogram is uniformly distributed .
Intensity variations in areas between periphery and central macular region of the eye have been reported to cause inaccuracy of vessel segmentation. [78] Based on the 2014 review, this technique was the most frequently used and appeared in 11 out of 40 recently (since 2011) published primary research. [77] Histogram Equalization Sample Image.
In image processing, normalization is a process that changes the range of pixel intensity values. Applications include photographs with poor contrast due to glare, for example.
Histogram equalization is a non-linear transform which maintains pixel rank and is capable of normalizing for any monotonically increasing color transform function. It is considered to be a more powerful normalization transformation than the grey world method.