Problem A  问题 A

For ocean exploration, clear and high-quality underwater images are crucial for deep-sea topography surveying and seabed resource investigation. However, in complex underwater environments, the image quality deteriorates due to phenomena such as absorption and scattering of light during its propagation in water, resulting in blurriness, low contrast, color distortion, etc. These conditions are referred to as underwater image degradation. The main causes of underwater image degradation include light propagation loss in water, forward scattering and backward scattering effects, as well as the scattering effect of suspended particles on light [5].
对于海洋勘探，清晰和高质量的水下图像对于深海地形测量和海底资源调查至关重要。然而，在复杂的水下环境中，由于光在水中传播过程中的吸收和散射等现象，图像质量变差，导致模糊、对比度低、颜色失真等。这些情况称为水下图像降级。水下图像退化的主要原因包括光在水中传播的损失、前向散射和后向散射效应，以及悬浮粒子对光的散射效应 [5]。

These factors collectively result in the loss of details and clarity during the transmission process of underwater images, affecting visual recognition and analysis.
这些因素共同导致水下图像在传输过程中丢失细节和清晰度，影响视觉识别和分析。

Figure 1. Schematic diagram of underwater image degradation, (a) shows green color cast, (b) shows blue color cast, © shows imaging blur, (d) shows insufficient light.
图 1.水下图像退化示意图，（a） 显示绿色偏，（b） 显示蓝色偏，©显示成像模糊，（d） 显示光线不足。

The schematic diagram of the underwater imaging process is shown in Figure 2. According to the Jaffe-McGlamery underwater imaging model, the underwater image captured by the camera can be represented as a linear combination of three components: direct component, forward scattering component, and backward scattering component [1]. Among them, the forward scattering component refers to the light that enters the imaging system after being scattered by suspended particles in water from target surface reflection or radiation. This component will cause blurring in the obtained image. The backward scattering component refers to the light that enters the imaging system after natural light entering water is scattered by suspended particles, resulting in low contrast in the obtained image. In general cases, due to close distance between objects and cameras, a simplified imaging model is used:
水下成像过程的示意图如图 2 所示。根据 Jaffe-McGlamery 水下成像模型，相机捕获的水下图像可以表示为三个分量的线性组合：直接分量、前向散射分量和后向散射分量 [1]。其中，前向散射分量是指光线在目标表面反射或辐射下被水中悬浮粒子散射后进入成像系统。此组件将导致获得的图像模糊。后向散射分量是指进入水中的自然光被悬浮粒子散射后进入成像系统的光，导致获得的图像对比度低。在一般情况下，由于物体和相机之间的距离很近，因此会使用简化的成像模型：

where $I(x)$$I(x)$I(x)I(x) represents the degraded underwater image, $J(x)$$J(x)$J(x)J(x) represents the clear image, $B$$B$BB is the ambient light in the underwater environment, and $t(x)$$t(x)$t(x)t(x) is the light transmission function of the underwater scene. The light transmission rate varies under different conditions. At the same time, the underwater ambient light also changes with factors such as depth and the turbidity of the water, all of which can lead to increased degradation of underwater images.
其中 $I(x)$$I(x)$I(x)I(x) ，表示降级的水下图像， $J(x)$$J(x)$J(x)J(x) 表示清晰的图像， $B$$B$BB 是水下环境中的环境光， $t(x)$$t(x)$t(x)t(x) 是水下场景的透光函数。透光率在不同条件下会有所不同。同时，水下环境光也会随着深度和水的浑浊度等因素而变化，所有这些都可能导致水下图像的退化加剧。

Figure 2. Conceptual diagram of underwater image degradation principle [6]
图 2.水下图像退化原理概念图 [6]
Before performing enhancement and other processing operations on underwater images, it is necessary to conduct statistical analysis on the image to be processed, as shown in Figure 3. Image analysis generally utilizes mathematical models combined with image processing techniques to analyze underlying features and higher-level structures, thereby extracting intelligent information. For example, using a histogram can statistically analyze the distribution of colors in different channels of the image, while applying edge operators can provide clarity information about object contours in the image. These pieces of information help us classify images into different categories and propose targeted solutions for image enhancement.
在对水下图像进行增强等处理操作之前，需要对需要处理的图像进行统计分析，如图 3 所示。图像分析通常利用数学模型与图像处理技术相结合来分析底层特征和更高级别的结构，从而提取智能信息。例如，使用直方图可以统计分析图像不同通道中的颜色分布，而应用边缘运算符可以提供有关图像中对象轮廓的清晰度信息。这些信息可帮助我们将图像分类为不同的类别，并提出有针对性的图像增强解决方案。

Figure 3. Color distribution curves of underwater images before and after using enhancement techniques, with each channel’s distribution curve being more balanced and closer to each other, thus improving the visual effect [7].
图 3.使用增强技术前后水下图像的色彩分布曲线，每个通道的分布曲线更加平衡且彼此更接近，从而提高视觉效果 [7]。

Underwater image enhancement technology is a technique that improves the quality of images captured in underwater environments by applying signal processing, image processing, and machine learning theories. It aims to reduce problems such as image blur, color distortion, and decreased contrast caused by the absorption and scattering of light in water, thereby improving the visibility and clarity of underwater images.
水下图像增强技术是一种通过应用信号处理、图像处理和机器学习理论来提高在水下环境中捕获的图像质量的技术。它旨在减少因水中光的吸收和散射而导致的图像模糊、颜色失真和对比度降低等问题，从而提高水下图像的可见性和清晰度。

Underwater image enhancement and restoration methods can be divided into traditional methods and deep learning methods. Traditional methods can be further categorized into non-physical models and physics-based models. Non-physical model methods improve visual quality by directly adjusting the pixel values of images, including applying existing image enhancement methods and specially designed algorithms. Physics-based model methods model and estimate parameters to invert the degradation process of underwater images. These methods can invert based on assumptions or prior knowledge, or they can use the optical properties of underwater imaging to improve the restored images. However, due to the complexity of underwater scenes, most existing methods cannot handle all scenarios. Therefore, an underwater scene enhancement algorithm tailored for complex scenarios is very important for subsequent tasks in underwater vision.
水下图像增强和修复方法可分为传统方法和深度学习方法。传统方法可以进一步分为非物理模型和基于物理的模型。非物理模型方法通过直接调整图像的像素值来提高视觉质量，包括应用现有的图像增强方法和专门设计的算法。基于物理的模型方法对参数进行建模和估计，以反转水下图像的退化过程。这些方法可以基于假设或先验知识进行反转，或者它们可以利用水下成像的光学特性来改进恢复的图像。但是，由于水下场景的复杂性，大多数现有方法无法处理所有场景。因此，为复杂场景量身定制的水下场景增强算法对于水下视觉中的后续任务非常重要。