Open AccessNon‐parametric modified histogram equalisation for contrast enhancement
Author(s) -
Poddar Shashi,
Tewary Suman,
Sharma Deewakar,
Karar Vinod,
Ghosh Ashish,
Pal Sankar K.
Publication year - 2013
Publication title -
iet image processing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.401
H-Index - 45
eISSN - 1751-9667
pISSN - 1751-9659
DOI - 10.1049/iet-ipr.2012.0507
Subject(s) - histogram , artificial intelligence , brightness , histogram equalization , pattern recognition (psychology) , entropy (arrow of time) , computer science , image (mathematics) , image histogram , mathematics , image quality , contrast (vision) , computer vision , algorithm , image texture , image processing , physics , quantum mechanics , optics
Histogram equalisation has been a much sought‐after technique for improving the contrast of an image, which however leads to an over enhancement of the image, giving it an unnatural and degraded appearance. In this framework, a generalised contrast enhancement algorithm is proposed which is independent of parameter setting for a given dynamic range of the input image. The algorithm uses the modified histogram for spatial transformation on grey scale to render a better quality image irrespective of the image type. Added to this, two variants of the proposed methodology are presented, one of which preserves the brightness of original image while the other variant increases the image brightness adaptively, giving it a better look. Qualitative and quantitative assessments like degree of entropy un‐preservation, edge‐based contrast measure and structure similarity index measures are then applied to the 500 image data set for comparing the proposed algorithm with several existing state‐of‐the‐art algorithms. Experimental results show that the proposed algorithm produces better or comparable enhanced images than several algorithms.