Grey-level context-driven histogram equalisation

Histogram equalisation (HE), which redefines the distribution of grey-levels in image, is an important step in image processing to enhance the image quality. Until now, numerous HE techniques have been proposed, among which major numbers have focused on solving the problem of how the grey-levels in the histogram of an input image should be properly partitioned so that the image produced by collecting all equalisation results for the partitioned sub-histograms leads to the quality enhancement of image. However, the partition-based equalisation methods have an inherent limitation that it is not able to equalise a sub-histogram crossing a partition boundary, which is the main cause of image distortion. In this study, the authors propose a new HE method to overcome this limitation. Precisely, rather than constraining disjoint mapping ranges of the grey-levels among the partitions, they devise two enabling techniques: (i) a mapping range for each grey-level with no range-disjoint constraint and (ii) a mapping distance between two adjacent grey-levels to make a full exploitation of mapping flexibility of grey-levels. They embody the image's global intensity distribution of grey-levels in the first technique while they embody the image's context in the second one.