Investigating intrinsic degradation factors by multi-branch aggregation for real-world underwater image enhancement

Recently, improving the visual quality of underwater images has received extensive attentions in both computer vision and ocean engineering fields. However, existing works mostly focus on directly learning clear images from degraded observations but without careful investigations on the intrinsic degradation factors, thus require mass training data and lack generalization ability. In this work, we propose a new method, named Multi-Branch Aggregation Network (termed as MBANet) to partially address the above issue. Specifically, by analyzing underwater degradation factors from the perspective of both color distortions and veil effects, MBANet first constructs a multi-branch multi-variable architecture to obtain one intermediate coarse result and two degraded factors. We then establish a physical model inspired process to fully utilize our estimated degraded factors and thus obtain the desired clear output images. A series of evaluations on multiple datasets show the superiority of our method against existing state-of-the-art approaches, both in execution speed and accuracy. Furthermore, we demonstrate that our MBANet can significantly improve the performance of salience object detection in the underwater environment.