Model-driven deep unfolding approach to underwater image enhancement

Abstract

We propose a model-driven deep learning approach to underwater image enhancement that can take advantage of both model- and learning-based approaches. We first formulate a joint optimization problem with physical priors to estimate the transmission map and latent clear image. Then, we solve the optimization problem iteratively. At each iteration, the optimization variables and image priors are updated by closed-form solutions and learned deep neural networks, respectively. Experimental results show that the proposed algorithm outperforms state-of-the-art underwater image enhancement algorithms.