Underwater image dehaze using scene depth estimation with adaptive color correction
Xueyan Ding, Yafei Wang, Jun Zhang, Xianping Fu · OCEANS 2017 - Aberdeen · 2017
Underwater images suffer from poor visibility due to color casts and light scattering that caused by physical properties existing in underwater environments. Degraded underwater images lead to a low accuracy rate of underwater object detection and recognition. To solve this problem, a novel underwater image enhancement method which combines adaptive color correction and image dehazing based on atmospheric scattering model is proposed in this paper. As the most important component of dehazing model, a transmission map is derived from the color corrected image. Considering the exponential relationship between the transmission map and scene depth map, transmission map estimation will be naturally formulated into a scene depth map estimation problem. To predict scene depth map, a Convolutional Neural Network (CNN) is employed on image patches extracted from the color corrected image. The experimental results show that the proposed strategy improves the quality of underwater images efficiently and arrives at good results in underwater objects detection and recognition.