DEEP UNFOLDING NETWORK WITH PHYSICS-BASED PRIORS FOR UNDERWATER IMAGE ENHANCEMENT

We propose an underwater image enhancement algorithm that leverages both model- and learning-based approaches by unfolding an iterative algorithm. We first formulate the underwater image enhancement task as a joint optimization problem, based on the image formation model with physical model and underwater-related priors. Then, we solve the optimization problem iteratively. Finally, we unfold the iterative algorithm so that, at each iteration, the optimization variables and regularizers for image priors are updated by closed-form solutions and learned deep networks, respectively. Experimental results demonstrate that the proposed algorithm outperforms state-of-the-art underwater image enhancement algorithms.