Due to the light absorption and scattering, captured underwater images usually contain severe color distortion and contrast reduction. To address the above problems, we combine the merits of deep learning and conventional image enhancement technology to improve the underwater image quality. We first propose a two-branch network to compensate the global distorted color and local reduced contrast, respectively. Adopting this global-local network can greatly ease the learning problem, so that it can be handled by using a lightweight network architecture. To cope with the complex and changeable underwater environment, we then design a compressed-histogram equalization to complement the data-driven deep learning, in which the parameters are fixed after training. The proposed compression strategy is able to generate vivid results without introducing over-enhancement and extra computing burden. Experiments demonstrate that our method significantly outperforms several state-of-the-arts in both qualitative and quantitative qualities.

由于光的吸收和散射，捕获的水下图像通常包含严重的色彩失真和对比度降低。为了解决上述问题，我们结合了深度学习和传统图像增强技术的优点来改善水下图像质量。我们首先提出一个两分支网络，分别补偿全局失真的颜色和局部降低的对比度。采用这种全球本地网络可以极大地缓解学习问题，因此可以使用轻量级的网络体系结构进行处理。为了应对复杂多变的水下环境，我们然后设计了压缩直方图均衡化，以补充数据驱动的深度学习，其中在训练后固定参数。所提出的压缩策略能够产生生动的结果，而不会引起过度增强和额外的计算负担。实验表明，我们的方法在定性和定量质量上均明显优于几种最新技术。