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A novel gamma correction approach using optimally clipped sub-equalization for dark image enhancement

In this paper, an efficient statistical approach employing a highly adaptive gamma correction based on adaptively clipped and locally equalized histogram using mean-median statistical pair, is presented for the enhancement of low contrast dark images without losing their intrinsic features. For this purpose, linearly stretched intensity range segmentation, first based on median and mean distribution sub-histograms are derived for local equalization after optimal clipping. Later on, non-linear transformational mapping has been imposed by suitable gamma-correction using the required gamma value-set, which itself is derived by cumulative distribution of the intensity values in adaptively equalized histogram. The proposed methodology clearly outperforms the other state-of-the-art methods in terms of complexity as well as quantitative and qualitative performance; and hence, can be appreciably used for a wide and dynamic range of image-database belonging to various domains ranging from biomedical images to remotely sensed satellite images.