Underwater image enhancement framework and its application on an autonomous underwater vehicle platform
Tengyue Li, Shenghui Rong, Xueting Cao, Yongbin Liu, Long Chen, Bo He
Abstract
Underwater imaging has been increasingly employed in vision-based marine research. However, the inappropriate installation of a light source and the complex underwater environment will result in the uneven illumination and overexposure on the captured images. To address these issues, an underwater image enhancement framework for autonomous underwater vehicles platform is proposed, which consists of underwater light source optimization and illumination nonuniformity correction. The light source optimization method improves the imaging quality by computing an appropriate angle of the light casting. In this way, the center of the field of view is always well lit. In addition, an adaptive filter-based illumination correction algorithm is proposed to solve the uneven illumination caused by the artificial light source. During this process, image block segmentation and the measure of image enhancement index are applied to improve the adaptability and reduce the calculation errors of the filter parameters. A dataset with real underwater images collected under different natural conditions has been built and tested. The experimental results indicate that the proposed method is more adaptive and effective than the typical methods.