A non-convex gradient fidelity-based variational model for image contrast enhancement

We propose a novel image contrast enhancement method via non-convex gradient fidelity-based (NGF) variational model which consists of the data fidelity term and the NGF regularization. The NGF prior assumes that the gradient of the desired image is close to the multiplication of the gradient of the original image by a scale factor, which is adaptively proportional to the difference of their gradients. The presented variational model can be viewed as a data-driven alpha-rooting method in the gradient domain. An augmented Lagrangian method is proposed to address this optimization issue by first transforming the unconstrained problem to an equivalent constrained problem and then applying an alternating direction method to iteratively solve the subproblems. Experimental results on a number of images consistently demonstrate that the proposed algorithm can efficiently obtain visual pleasure results and achieve favorable performance than the current state-of-the-art methods.