  Despite recent advances in image enhancement, it remains difficult for
existing approaches to adaptively improve the brightness and contrast for both
low-light and normal-light images. To solve this problem, we propose a novel 2D
histogram equalization approach. It assumes intensity occurrence and
co-occurrence to be dependent on each other and derives the distribution of
intensity occurrence (1D histogram) by marginalizing over the distribution of
intensity co-occurrence (2D histogram). This scheme improves global contrast
more effectively and reduces noise amplification. The 2D histogram is defined
by incorporating the local pixel value differences in image reflectance into
the density estimation to alleviate the adverse effects of dark lighting
conditions. Over 500 images were used for evaluation, demonstrating the
superiority of our approach over existing studies. It can sufficiently improve
the brightness of low-light images while avoiding over-enhancement in
normal-light images.
