Abstract
Underwater images suffer from color shift, low contrast, and blurred details as a result of the absorption and scattering of light in the water. Degraded quality images can significantly interfere with underwater vision tasks. The existing data-driven based underwater image enhancement methods fail to sufficiently consider the impact related to the inconsistent attenuation of spatial areas and the degradation of color channel information. In addition, the dataset used for model training is small in scale and monotonous in the scene. Therefore, our approach solves the problem from two aspects of the network architecture design and the training dataset. We proposed a fusion attention block that integrate the non-local modeling ability of the Swin Transformer block into the local modeling ability of the residual convolution layer. Importantly, it can adaptively fuse non-local and local features carrying channel attention. Moreover, we synthesize underwater images with multiple water body types and different degradations using the underwater imaging model and adjusting the degradation parameters. There are also perceptual loss functions introduced to improve image vision. Experiments on synthetic and real-world underwater images have shown that our method is superior. Thus, our network is suitable for practical applications.