Histogram equalization: Difference between revisions

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Modifications of this method use multiple histograms, called subhistograms, to emphasize local contrast rather than overall global contrast. Examples of such methods include [[adaptive histogram equalization]] and variations including, contrast limited adaptive histogram equalization, multipeak histogram equalization, and multipurpose beta-optimized bihistogram equalization (MBOBHE). The goal of these methods, especially MBOBHE, is to modifying the algorithm to improve the contrast without producing brightness mean-shift and detail loss artifacts.<ref>{{cite journal|last1=Hum|first1=Yan Chai|last2=Lai|first2=Khin Wee|last3=Mohamad Salim|first3=Maheza Irna|title=Multiobjectives bihistogram equalization for image contrast enhancement|journal=Complexity|date=11 October 2014|volume=20|issue=2|pages=22–36|doi=10.1002/cplx.21499|bibcode=2014Cmplx..20b..22H}}</ref>

A signal transform equivalent to histogram equalization also seems to happen in [[biological neural networks]] so as to maximize the output firing rate of the neuron as a function of the input statistics. This has been proved in particular in the [[fly]] [[retina]].<ref>{{cite journal|last=Laughlin|first=S.B|year=1981|title=A simple coding procedure enhances a neuron's information capacity|journal=Z. Naturforsch.|volume=9–10(36):910–2}}</ref><!--[[User:Kvng/RTH]]-->

===Back projection===

The '''back projection''' (or "project") of a histogrammed image is the re-application of the modified histogram to the original image, functioning as a look-up table for pixel brightness values.

For each group of pixels taken from the same position from all input single-channel images, the function puts the histogram bin value to the destination image, where the coordinates of the bin are determined by the values of pixels in this input group. In terms of statistics, the value of each output image pixel characterizes the probability that the corresponding input pixel group belongs to the object whose histogram is used.<ref>{{cite manual|year=2001|title=Open Source Computer Vision Library Reference Manual|url=http://www.cs.unc.edu/~stc/FAQs/OpenCV/OpenCVReferenceManual.pdf|archive-url=https://web.archive.org/web/20150409155114/http://www.cs.unc.edu/~stc/FAQs/OpenCV/OpenCVReferenceManual.pdf|url-status=dead|archive-date=April 9, 2015|author=Intel Corporation|access-date=2015-01-11}}</ref><!--[[User:Kvng/RTH]]-->

==Implementation==