Progressive multi-branch embedding fusion network for underwater image enhancement

Abstract

The underwater image enhancement techniques are essential for ocean research and engineering applications. In this paper, we propose a progressive multi-branch embedding fusion network (PMEFN) to improve image quality. Specifically, a multi-branch embedding fusion module (MEFM) is designed. The distorted images and its sharpened versions are used as the input, which are fused to learn the contextualized features based on a two-branch hybrid encoder–decoder module (HEDM2) combined with the triple attention module to focus on the noise region. Afterwards, we use the multi-stage refining framework to decompose the image enhancement or marine snow removal tasks into multiple stages and progressively learn the nonlinear functions from the distorted inputs. Additionally, the outputs generated at each stage are further refined and enhanced based on a three-branch hybrid encoder–decoder module (HEDM3). We perform experiments using real underwater datasets, including EUVP, UFO-120, UIEB, and synthetic dataset MSRB. The experimental results show that the proposed method has a superior performance as compared to other methods in terms of quantitative performance and visual quality. In addition, the effectiveness of each component is further validated by performing ablation experiments.