Attenuation Coefficient Guided Two-Stage Network for Underwater Image Restoration

Underwater images suffer from severe color casts, low contrast and blurriness, which are caused by scattering and absorption when light propagates through water. However, existing deep learning methods treat the restoration process as a whole and do not fully consider the underwater physical distortion process. Thus, they cannot adequately tackle both absorption and scattering, leading to poor restoration results. To address this problem, we propose a novel two-stage network for underwater image restoration (UIR), which divides the restoration process into two parts viz. horizontal and vertical distortion restoration. In the first stage, a model-based network is proposed to handle horizontal distortion by directly embedding the underwater physical model into the network. The attenuation coefficient, as a feature representation in characterizing water type information, is first estimated to guide the accurate estimation of the parameters in the physical model. For the second stage, to tackle vertical distortion and reconstruct the clear underwater image, we put forth a novel attenuation coefficient prior attention block (ACPAB) to adaptively recalibrate the RGB channel-wise feature maps of the image suffering from the vertical distortion. Experiments on both synthetic dataset and real-world underwater images demonstrate that our method can effectively tackle scattering and absorption compared with several state-of-the-art methods.