Underwater images suffer from color casts and low contrast due to wavelength- and distance-dependent attenuation and scattering. To solve these two degradation issues, we present an underwater image enhancement network via medium transmission-guided multi-color space embedding, called Ucolor . Concretely, we first propose a multi-color space encoder network, which enriches the diversity of feature representations by incorporating the characteristics of different color spaces into a unified structure. Coupled with an attention mechanism, the most discriminative features extracted from multiple color spaces are adaptively integrated and highlighted. Inspired by underwater imaging physical models, we design a medium transmission (indicating the percentage of the scene radiance reaching the camera)-guided decoder network to enhance the response of network towards quality-degraded regions. As a result, our network can effectively improve the visual quality of underwater images by exploiting multiple color spaces embedding and the advantages of both physical model-based and learning-based methods. Extensive experiments demonstrate that our Ucolor achieves superior performance against state-of-the-art methods in terms of both visual quality and quantitative metrics. The code is publicly available at: https://li-chongyi.github.io/Proj_Ucolor.html .

中文翻译：

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通过中等透射率的多色空间嵌入进行水下图像增强

水下图像由于依赖于波长和距离的衰减和散射而遭受偏色和低对比度的困扰。为了解决这两个退化问题，我们提出了一种通过介质传输引导的多色空间嵌入的水下图像增强网络，称为乌色 。具体而言，我们首先提出一种多色彩空间编码器网络，该网络通过将不同色彩空间的特征整合到一个统一的结构中，从而丰富了特征表示的多样性。结合注意机制，将从多个颜色空间中提取的最具区别性的特征自适应地集成并突出显示。受水下成像物理模型的启发，我们设计了一种介质传输（指示到达摄像机的场景辐射率的百分比）引导的解码器网络，以增强网络对质量下降区域的响应。因此，我们的网络可以利用多种颜色空间嵌入以及基于物理模型的方法和基于学习的方法的优点，有效地改善水下图像的视觉质量。大量的实验表明，我们的乌色在视觉质量和定量指标方面，与最新方法相比，具有卓越的性能。该代码可在以下位置公开获得：https://li-chongyi.github.io/Proj_Ucolor.html 。