A Novel High-Turbidity Underwater Image Quality Assessment Method
Huimin Lu, Yujie Li, Xin Shane Li, Xing Xu, Li He, Shenglin Mu, Shota Nakashima, Yun Li, Xuelong Hu, Seiichi Serikawa · 2016
Vision-based underwater navigation and object detection requires robust computer vision algorithms to operate in turbid water. Many conventional methods aimed at improving visibility in low turbid water. In this paper, we propose a novel contrast enhancement measurement for different enhancement methods' assessment. As a rule to compare the performance of different image enhancement algorithms, a more comprehensive image quality assessment index Q is proposed. The index combines the benefits of SSIM index and colour distance index. Experimental results show that the proposed approach statistically outperforms state-of-the-art general purpose underwater image contrast enhancement algorithms.