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8 October 2015 Using near infrared light for deep sea mining observation systems
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Proceedings Volume 9675, AOPC 2015: Image Processing and Analysis; 967503 (2015) https://doi.org/10.1117/12.2196779
Event: Applied Optics and Photonics China (AOPC2015), 2015, Beijing, China
Abstract
In this paper, we design a novel deep-sea near infrared light based imaging equipment for deep-sea mining observation systems. The spectral sensitivity peaks are in the red region of the invisible spectrum, ranging from 750nm to 900nm. In addition, we propose a novel underwater imaging model that compensates for the attenuation discrepancy along the propagation path. The proposed model fully considered the effects of absorption, scattering and refraction. We also develop a locally adaptive Laplacian filtering for enhancing underwater transmission map after underwater dark channel prior estimation. Furthermore, we propose a spectral characteristic-based color correction algorithm to recover the distorted color. In water tank experiments, we made a linear scale of eight turbidity steps ranging from clean to heavily scattered by adding deep sea soil to the seawater (from 500 to 2000 mg/L). We compared the results of different turbidity underwater scene, illuminated alternately with near infrared light vs. white light. Experiments demonstrate that the enhanced NIR images have a reasonable noise level after the illumination compensation in the dark regions and demonstrates an improved global contrast by which the finest details and edges are significantly enhanced. We also demonstrate that the effective distance of the designed imaging system is about 1.5 meters, which can meet the requirement of micro-terrain observation around the deep-sea mining systems. Remotely Operated Underwater Vehicle (ROV)-based experiments also certified the effectiveness of the proposed method.
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Huimin Lu, Yujie Li, Xin Li, Jianmin Yang, and Seiichi Serikawa "Using near infrared light for deep sea mining observation systems", Proc. SPIE 9675, AOPC 2015: Image Processing and Analysis, 967503 (8 October 2015); https://doi.org/10.1117/12.2196779
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