In recent years, deep sea and ocean explorations have attracted more attention in the marine industry. Most of the marine vehicles, including robots, submarines, and ships, would be equipped with automatic imaging of deep sea layers. There is a reason which the quality of the images taken by the underwater devices is not optimal due to water properties and impurities. Consequently, water absorbs a series of colors, so processing gets more difficult. Scattering and absorption are related to underwater imaging light and are called light attenuation in water. The examination has previously shown that the emergence of some inherent limitations is due to the presence of artifacts and environmental noise in underwater images. As a result, it is hard to distinguish objects from their backgrounds in those images in a real-time system. This paper discusses the effect of the software and hardware parts for the underwater image, surveys the state-of-art different strategies and algorithms in underwater image enhancement, and measures the algorithm performance from various aspects. We also consider the important conducted studies on the field of quality enhancement in underwater images. We have analyzed the methods from five perspectives: (a) hardware and software tools, (b) a variety of underwater imaging techniques, (c) improving real-time image quality, (d) identifying specific objectives in underwater imaging, and (e) assessments. Finally, the advantages and disadvantages of the presented real/non-real-time image processing techniques are addressed to improve the quality of the underwater images. This systematic review provides an overview of the major underwater image algorithms and real/non-real-time processing.

近年来，深海和海洋勘探在海洋工业中吸引了更多关注。大多数海洋车辆，包括机器人，潜水艇和轮船，都将配备自动成像的深海层。有一个原因是由于水的特性和杂质，水下设备所拍摄图像的质量不是最佳的。因此，水吸收了一系列颜色，因此加工变得更加困难。散射和吸收与水下成像光有关，被称为水中的光衰减。先前的检查表明，某些固有限制的出现是由于水下图像中存在伪影和环境噪声所致。结果，在实时系统中很难在那些图像中将对象与其背景区分开。本文讨论了软件和硬件部分对水下图像的影响，概述了水下图像增强技术的最新策略和算法，并从各个方面评估了算法的性能。我们还考虑了有关水下图像质量增强领域的重要研究。我们从五个角度分析了这些方法：（a）硬件和软件工具，（b）各种水下成像技术，（c）改善实时图像质量，（d）确定水下成像的特定目标，以及（e ）评估。最后，解决了所提出的实时/非实时图像处理技术的优缺点，以提高水下图像的质量。