Perceptually motivated enhancement method for non-uniformly illuminated images
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, the authors propose a perceptually inspired image enhancement method, which treats an image as a product of a luminance mapping (LM) transfer function and a contrast measure (CM) transfer function. The contribution of this proposed method is two-fold. Firstly, they propose a progressive LM transfer function based on the sensitivity of the human visual system to emphasise changes at low brightness level and attenuates changes at high brightness levels. Secondly, they introduce a CM 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.