自提出海洋强国战略以来，水下图像清晰化已成为机器视觉领域的研究热点和挑战。与陆地良好环境中拍摄的图像不同，光在水下传播时受水下介质吸收和散射影响，导致水下图像存在颜色失真、细节模糊、低对比度、亮度不均等问题。低质量水下图像使得水下机器视觉感知面临看不清的挑战，因此研究有效的水下图像增强方法是当前水下视觉领域亟待解决的重要问题。本文在广泛调研的基础上梳理了水下图像质量提升的方法，主要包括图像复原和图像增强两大类。随后，深入讨论了代表性方法，并通过定性和定量评估分析，详细探讨了这些方法的优势和局限性。此外，全面概述了水下图像质量退化问题、提升方法、数据集及评估指标，分析了其在不同场景下的性能，为读者提供了全面的水下图像清晰化方法研究现状。最后，根据水下图像增强与复原方法的难点与目前面临的主要问题对研究方向和发展趋势进行了归纳和展望。这一综合性研究为未来相关领域的研究和实践提供了有力的指导。

Since the inception of the marine power strategy, there has been an increasing focus on an investigation into the quality of underwater images in the marine environment. However, unlike images captured in favorable terrestrial conditions, light propagation underwater is influenced by the absorption and scattering of the underwater medium. Light absorption can result in color distortion, reduced contrast, and diminished brightness in underwater images, while light scattering may cause haziness, loss of details, and noise amplification. The challenge posed by low-quality underwater images hinders effective machine vision in underwater environments. Therefore, researching effective methods for enhancing underwater machine vision has become a critical issue in the current field of underwater vision. This holds significant theoretical and practical significance for strengthening marine technological capabilities and promoting the sustained and healthy development of the marine economy. This paper provides a comprehensive overview of existing underwater image clarification methods, highlighting the strengths and disadvantages of each approach. For instance, image restoration based on methods relies on prior assumptions, but an excess of prior knowledge can result in difficulties with multi-parameter optimization and sensitivity to robustness. On the other hand, image enhancement based on methods only considers the pixel information of the image, lacking consideration for the imaging model, thereby risking noise amplification and local over-enhancement. Consequently, designing simple yet effective methods for underwater image clarification is crucial for improving the quality of underwater images. This paper provides a comprehensive overview of methods to enhance the quality of underwater images through an extensive exploration of image restoration and image enhancement techniques. It concludes with a summary of the methods and their merits and demerits. Regarding image restoration, the methods are categorized into four types: underwater optical imaging, polarization characteristics, prior knowledge, and deep learning. Optical imaging methods primarily consider the optical properties of the water itself, accounting for phenomena such as light attenuation, scattering, and absorption in the underwater environment. These methods rely on physical optical models to characterize underwater light propagation. Polarization characteristic methods involve collecting polarized images from the same scene, separating background light and scattered light, estimating light intensity and transmittance, and inversely obtaining clarified images. Prior methods guide image processing through prior information, and deep learning methods utilize deep neural network models to restore underwater images. For image enhancement-based methods, the overview includes frequency-domain, spatial-domain, color constancy, fusion-based, and deep learning methods. Frequency-domain methods process underwater images through convolution or spatial transformations to achieve enhancement. Spatial-domain methods directly act on image pixels, altering their intrinsic characteristics through techniques like grayscale mapping, effectively improving image contrast and detail. Color constancy methods enhance images by leveraging color consistency present in the image. Fusion methods apply multiple algorithms to a single input image, generating enhanced versions. Subsequently, fusion weights are calculated for these enhanced images, and the final enhanced image is generated through image fusion. Regarding deep learning-based methods, the summary covers CNN-based and GAN-based approaches. CNN methods employ convolutional neural networks to enhance underwater images by learning image features, structure, and deep network processing. GAN methods utilize generator and discriminator components in a generative adversarial network to enhance and restore underwater images. The paper then delves into a detailed discussion of each method"s innovations, advantages, and limitations, summarizing the above methods comprehensively. Additionally, several commonly used underwater datasets are introduced. Furthermore, a qualitative and quantitative analysis is conducted on representative clarity methods. This paper provides a comprehensive overview and summary of the degradation issues in underwater images, methods for underwater image clarification, underwater image datasets, and underwater image quality assessment. We selected eleven classical underwater image clarity methods (Image Restoration Methods: GIFM, BRU, GDCP, and ULAP; Image Enhancement Methods: WWPF, PCDE, and ACDC; Deep Learning Methods: PUIE-Net, SGUIE-Net, LANet, and CLUIE-Net) and tested them on standard underwater datasets. We compared and analyzed these methods using five quantitative evaluation metrics (AG, IE, PCQI, UIQM, and UCIQE). Through qualitative and quantitative comparative analyses, we summarized the strengths and weaknesses of these representative clarity methods and underwater image quality assessment methods, better understanding the current research status in underwater image clarification and outlining future development prospects. This study offers a comprehensive review of methods aimed at enhancing and restoring underwater images. It underscores the significance of enhancing image quality and underscores the scientific and economic potential of underwater image clarification methods in applications such as marine resource development. The study serves as a valuable guide for future research and practices in related fields.