Underwater Image Restoration using Deep Networks to Estimate Background Light and Scene Depth
Keming Cao, Yan‐Tsung Peng, Pamela C. Cosman · 2018
Images taken underwater often suffer color distortion and low contrast because of light scattering and absorption. An underwater image can be modeled as a blend of a clear image and a background light, with the relative amounts of each determined by the depth from the camera. In this paper, we propose two neural network structures to estimate background light and scene depth, to restore underwater images. Experimental results on synthetic and real underwater images demonstrate the effectiveness of the proposed method.