LAUSR

Vision-guided autonomous underwater vehicles based on remote sensing play an important role in ocean missions. However, some problems exist in underwater visual perception, such as color distortion, low contrast, and fuzzy details, which restrict the applications of underwater visual tasks. Most of the state-of-the-art image enhancement methods are still limited in scene adaptability, recovery accuracy, and real-time processing. To solve these problems, we propose an underwater sensing scene image enhancement method called a multiscale feature fusion network (MFFN). To extract the multiscale feature, the measure merging the feature extraction module, the feature fusion module, and the attention reconstruction module is designed. This measure can also enhance the adaptability and visual effect of the scene. Moreover, we propose multiple objective functions for supervised training to match the nonlinear mapping. Based on the qualitative and quantitative evaluations, the proposed method produces competitive performance compared with some state-of-the-art methods, and the perception and statistical quality of underwater images are enhanced effectively.