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.

中文翻译：

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水下图像增强框架及其在自主水下航行器平台上的应用

摘要。水下成像越来越多地用于基于视觉的海洋研究。但是，光源安装不当，以及复杂的水下环境，都会导致拍摄的图像出现光照不均、曝光过度等问题。针对这些问题，提出了一种用于自主水下航行器平台的水下图像增强框架，该框架由水下光源优化和光照不均匀性校正组成。光源优化方法通过计算适当的投光角度来提高成像质量。这样，视野的中心总是光线充足。此外，针对人工光源造成的光照不均问题，提出了一种基于自适应滤波器的光照校正算法。在这个过程中，应用图像块分割和图像增强指数的度量来提高自适应性，减少滤波器参数的计算误差。建立并测试了在不同自然条件下收集的真实水下图像数据集。实验结果表明，所提出的方法比典型方法更具适应性和有效性。