Restoration on High Turbidity Water Images Under Near-Field Illumination Using a Light-Field Camera
Shijun Zhou, Zhen Zhang, Yajing Liu, Jiandong Tian · IEEE Transactions on Computational Imaging · 2024
Restoring underwater degraded images necessitates accurate estimation of backscatter. Prior research commonly treats backscatter as a constant value across channels. However, addressing backscatter removal becomes intricate when images are captured under conditions of near-field illumination and within densely scattered mediums. In these scenarios, the approximation of backscatter by constant values falls short of efficacy. This paper presents an innovative methodology for characterizing backscatter distribution using curved surfaces while taking into account the scattering conditions at the pixel level. Unlike the previous methods that employ the atmosphere scattering model, we introduce an adaptative function to describe backscatter distribution. By capitalizing on the capabilities of light field cameras in recording light directions, we devise a solution to the focus problem encountered in turbid water environments. Through shear and refocus operations, we not only achieve denoising but also elevate overall image quality. The experimental results clearly demonstrate that our method outperforms state-of-the-art approaches in terms of both visual quality and quantitative metrics.