Non‐parametric modified histogram equalisation for contrast enhancement

Abstract

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.

An innovative technique for contrast enhancement of computed tomography images using normalized gamma-corrected contrast-limited adaptive histogram equalization

“…This employs the image fuzzy statistics resulting in a better handling of the gray-level imprecise values to produce an improved image contrast. After that, a non-parametric modified histogram equalization (NMHE) was introduced [13], which owns an independent parameter setting for an image dynamic range. In addition, it employs an amended histogram function to produce an improved image quality.…”

“…Therefore, specially designed techniques should be applied to obtain a better image resolution without any information about the origin of the source degradation. These techniques are mainly classified as spatial and frequency domain techniques [13]. The most popular contrast enhancement methods are the ones that improve the gray-levels of the image in the spatial domain.…”

Histogram equalization and optimal profile compression based approach for colour image enhancement

“…Practically, this is realized in the form of histogram modifications [6]. Variations in this class of methods include incorporating local features around a neighbourhood for each pixel to aid equalization [7] or through the matching of an input histogram to a target histogram.…”

Semi dynamic fuzzy histogram equalization

“…Despite of its popularity, CHE normally introduces undesirable visual artifacts in the processed images due to either excessive brightness shift [2][3], or over enhancement of noisy regions [4][5], or saturation of intensities [6][7]. To overcome these limitations of CHE method, numerous solutions have been suggested in the literature [2][3][4][5][6][7][8][9][10][11].…”

“…To overcome these limitations of CHE method, numerous solutions have been suggested in the literature [2][3][4][5][6][7][8][9][10][11].…”