Dual-channel prior-based deep unfolding with contrastive learning for underwater image enhancement

Abstract

Underwater image enhancement (UIE) techniques aim to improve the visual quality of underwater images degraded by wavelength-dependent light absorption and scattering. In this work, we propose a deep unfolding approach for UIE to leverage the advantages of both model-and learning-based approaches while overcoming their weaknesses. Specifically, we first formulate the UIE task as a joint optimization problem with physics-based priors, providing a robust theoretical foundation on the properties of underwater imaging. Then, we define implicit regularizers to compensate for modeling inaccuracies in the physics-based priors and solve the optimization using an iterative technique. Finally, we unfold the iterative algorithm into a series of interconnected blocks, where each block represents a single iteration of the algorithm. We further improve performance by employing a contrastive learning strategy that learns discriminative representations between the underwater and clean images. Experimental results demonstrate that the proposed algorithm provides better enhancement performance than state-of-the-art algorithms.