Enhancing the contrast of the grey-scale image based on meta-heuristic optimization algorithm

Image contrast enhancement (ICE) is an important step in image processing and analysis as the quality of an image plays a pivotal role in human understanding. Moreover, contrast is considered a key aspect for the assessment of picture quality. Incomplete beta function (IBF) is one of the widely used transformations and histogram equalization (HE) is also one of the most popular methods used for this task. However, HE has some limitations as the local contrast of an image cannot be uniformly enhanced. In the present work, a contrast enhancement method is proposed for grey-scale images based on a recent socio-inspired meta-heuristic called political optimizer (PO). The PO algorithm follows the multi-phased process of politics. The exploitative capability of PO is improved by combining it with the adaptive β-hill climbing (AβHC) which is regarded as one of the best local search techniques. The hybridization of these two algorithms is then used to find the optimal values of pixels which can intensify the hidden characteristic of the low-contrast images. The proposed algorithm is tested over a publicly available Kodak image dataset along with some standard images and evaluated in terms of standard metrics. The experimental results demonstrate that the proposed method can successfully outperform many existing methods considered here for comparison.