%0 Journal Article %1 IJACSA.2011.020721 %A Manpreet Kaur Jasdeep Kaur, Jappreet Kaur %D 2011 %J International Journal of Advanced Computer Science and Applications(IJACSA) %K brightness contrast enhancement enhancement; equalization; error; histogram image mean minimum partition.,component preserving processing; %N 7 %T Survey of Contrast Enhancement Techniques based on Histogram Equalization %U http://ijacsa.thesai.org/ %V 2 %X This Contrast enhancement is frequently referred to as one of the most important issues in image processing. Histogram equalization (HE) is one of the common methods used for improving contrast in digital images. Histogram equalization (HE) has proved to be a simple and effective image contrast enhancement technique. However, the conventional histogram equalization methods usually result in excessive contrast enhancement, which causes the unnatural look and visual artifacts of the processed image. This paper presents a review of new forms of histogram for image contrast enhancement. The major difference among the methods in this family is the criteria used to divide the input histogram. Brightness preserving Bi-Histogram Equalization (BBHE) and Quantized Bi-Histogram Equalization (QBHE) use the average intensity value as their separating point. Dual Sub-Image Histogram Equalization (DSIHE) uses the median intensity value as the separating point. Minimum Mean Brightness Error Bi-HE (MMBEBHE) uses the separating point that produces the smallest Absolute Mean Brightness Error (AMBE). Recursive Mean-Separate Histogram Equalization (RMSHE) is another improvement of BBHE. The Brightness preserving dynamic histogram equalization (BPDHE) method is actually an extension to both MPHEBP and DHE. Weighting mean-separated sub-histogram equalization (WMSHE) method is to perform the effective contrast enhancement of the digital image.

@article{IJACSA.2011.020721, abstract = {This Contrast enhancement is frequently referred to as one of the most important issues in image processing. Histogram equalization (HE) is one of the common methods used for improving contrast in digital images. Histogram equalization (HE) has proved to be a simple and effective image contrast enhancement technique. However, the conventional histogram equalization methods usually result in excessive contrast enhancement, which causes the unnatural look and visual artifacts of the processed image. This paper presents a review of new forms of histogram for image contrast enhancement. The major difference among the methods in this family is the criteria used to divide the input histogram. Brightness preserving Bi-Histogram Equalization (BBHE) and Quantized Bi-Histogram Equalization (QBHE) use the average intensity value as their separating point. Dual Sub-Image Histogram Equalization (DSIHE) uses the median intensity value as the separating point. Minimum Mean Brightness Error Bi-HE (MMBEBHE) uses the separating point that produces the smallest Absolute Mean Brightness Error (AMBE). Recursive Mean-Separate Histogram Equalization (RMSHE) is another improvement of BBHE. The Brightness preserving dynamic histogram equalization (BPDHE) method is actually an extension to both MPHEBP and DHE. Weighting mean-separated sub-histogram equalization (WMSHE) method is to perform the effective contrast enhancement of the digital image.}, added-at = {2014-02-21T08:00:08.000+0100}, author = {{Manpreet Kaur Jasdeep Kaur}, Jappreet Kaur}, biburl = {https://www.bibsonomy.org/bibtex/29008dc5f64d21a847f04e48df0247bdc/thesaiorg}, interhash = {bf8dcbcbc3ffaef304c4d7a629796b53}, intrahash = {9008dc5f64d21a847f04e48df0247bdc}, journal = {International Journal of Advanced Computer Science and Applications(IJACSA)}, keywords = {brightness contrast enhancement enhancement; equalization; error; histogram image mean minimum partition.,component preserving processing;}, number = 7, timestamp = {2014-02-21T08:00:08.000+0100}, title = {{Survey of Contrast Enhancement Techniques based on Histogram Equalization}}, url = {http://ijacsa.thesai.org/}, volume = 2, year = 2011 }