Joint optimization and perceptual boosting of global and local contrast for efficient contrast enhancement

Contrast enhancement which aims to increase the contrast of an image with low dynamic range, has been widely studied and exploited. In spite of the great success of many contrast enhancement algorithms, they still have difficulty in achieving both global and local contrast enhancement so that some over-enhancement, under-enhancement or even halo artifacts are often produced in complex images. This paper proposes a simple but efficient contrast enhancement method which may achieve both global and local contrast enhancement and perceptually suppress the above-mentioned problems. A cost function integrating global enhancement with local contrast enhancement is firstly constructed to pose image enhancement as an optimization problem. Then, two key steps are involved in solving for an optimal solution, the just-noticeable difference (JND) model is introduced to perceptually determine maximum local gains and neighbouring gray-level difference for local and global contrast enhancement, respectively, and an adaptive parameter regularization method is invoked to further suppress over-enhancement and halo artifacts. The experimental results on many images both qualitatively and quantitatively demonstrate our algorithm can robustly provide better visual quality in global and local contrast compared to a selection of other well-known state-of-the-art algorithms.