Underwater image enhancement is an important low-level computer vision task for autonomous underwater vehicles and remotely operated vehicles to explore and understand the underwater environments. Recently, deep convolutional neural networks (CNNs) have been successfully used in many computer vision problems, and so does underwater image enhancement. There are many deep-learning-based methods with impressive performance for underwater image enhancement, but their memory and model parameter costs are hindrances in practical application. To address this issue, we propose a lightweight adaptive feature fusion network (LAFFNet). The model is the encoder-decoder model with multiple adaptive feature fusion (AAF) modules. AAF subsumes multiple branches with different kernel sizes to generate multi-scale feature maps. Furthermore, channel attention is used to merge these feature maps adaptively. Our method reduces the number of parameters from 2.5M to 0.15M (around 94% reduction) but outperforms state-of-the-art algorithms by extensive experiments. Furthermore, we demonstrate our LAFFNet effectively improves high-level vision tasks like salience object detection and single image depth estimation.

翻译：水下图像增强是自主水下飞行器和遥控潜水器探索和理解水下环境的重要低级计算机愿景任务。最近,许多计算机视觉问题成功地使用了深演神经网络(CNNs),水下图像增强也是如此。在水下图像增强方面有许多深层学习方法,其性能令人印象深刻,但其内存和模型参数成本是实际应用的障碍。为解决这一问题,我们建议建立一个轻量适应性特异聚合网络(LAFFNet) 。模型是具有多个适应性特征聚合模块的编码器-解密器模型。AAAAF将多个不同内核大小的分支子进行子集成,以生成多尺度的地貌图。此外,还利用频道关注将这些特性图进行适应性合并。我们的方法将参数从2.5M(约减少94% ) 减少到 0.15M(但通过广泛的实验而超出最新水平的算法。此外,我们展示了我们的LAFFNet有效地改进了高层次的视觉任务,如突出的物体探测和单一图像深度估计。