Equilibrating the impact of fluid scattering attenuation on underwater optical imaging via adaptive parameter...

Fluid scattering attenuation is a pervasive and intricate physical phenomenon in underwater environment, which challenges optical imaging for various visual tasks. This paper presents a Learnable Physical Imaging Model (LPIM) for equilibrating the impact of the fluid scattering attenuation on underwater optical imaging. Whereas the imaging medium has the liquidity and diversity, an adaptive physical imaging framework is proposed to learn the nonlinear degradation parameter for various complex underwater environment. This adaptive imaging model is jointed by adversarial competition and directional learning strategy, which makes our model learn more excellent properties from the unpaired referring image, and guides the restoration network to directionally adjust the imaging model parameters. To describe the real scenarios better, a multi-branch network, i.e., R-Net, D-Net and BN-net, is developed to learn and capture the color, light intensity and attenuation information. Experimental results on several datasets demonstrate that our approach outperforms the state-of-the-arts in both color balance and visual effect. The LPIM provides an innovative framework designed to counteract image degradation resulting from fluid scattering and attenuation.