A depth estimation model from a single underwater image with non-uniform illumination correction
Shichang Zhang, Xiaofei Gong, Rui Nian, Bo He, Yaomin Wang, Amaury Lendasse · OCEANS 2017 - Aberdeen · 2017
Underwater visual understanding tends to be one of the most important challenges in ocean investigations recent years. In this paper, we make an attempt to develop a depth estimation approach from one single underwater image with non-uniform illumination correction. First, we try to remove the relatively strong reflection layer from the scene layer in those underwater images with non-uniform illumination and rely on the saturation detection to characterize the local regions of the artificial illumination in underwater images for compensation in depth estimation. Then we try to implement underwater dark channel prior and basically consider the blue and green color channels as the underwater visual source, and after one veilling light evaluation in the underwater scene layer, we further adopt one kind of fast guided filter into the rough transmission with low saturation compensation for illumination correction to refine a more dedicated depth estimation. In the simulation experiment, we propose that the proposed scheme can achieve good performance in ocean depth estimation with the help of uneven illumination correction.