Non-uniformly illuminated images often suffer from low visibility in dark areas. Traditional methods usually enhance non-uniformly illuminated images by bringing out the details in the dark areas, but easily result in over-enhancement. Motivated by the Weber contrast model, we proposes a perceptually inspired image enhancement method, which treats an image as a product of a luminance mapping transfer function and a contrast measure transfer function. The contribution of this proposed method is two-fold. Firstly, we propose a progressive luminance mapping transfer function based on the sensitivity of the human visual system to emphasize changes at low brightness level and attenuates changes at high brightness levels. Secondly, we introduce a contrast measure transfer function, which is based on a special implementation of a neural model of the human visual receptive field, to improve local intensity contrast. Experimental comparisons with some state-of the–art methods show that the proposed method can achieve both contrast enhancement and visual fidelity preservation.
Paper and Demo: Paper and demo