Successful applications of complex vision-based behaviours underwater have lagged behind progress in terrestrial and aerial domains. This is largely due to the degraded image quality resulting from the physical phenomena involved in underwater image formation. Spectrally-selective light attenuation drains some colors from underwater images while backscattering adds others, making it challenging to perform vision-based tasks underwater. State-of-the-art methods for underwater color correction optimize the parameters of image formation models to restore the full spectrum of color to underwater imagery. However, these methods have high computational complexity that is unfavourable for realtime use by autonomous underwater vehicles (AUVs), as a result of having been primarily designed for offline color correction. Here, we present DeepSeeColor, a novel algorithm that combines a state-of-the-art underwater image formation model with the computational efficiency of deep learning frameworks. In our experiments, we show that DeepSeeColor offers comparable performance to the popular "Sea-Thru" algorithm (Akkaynak & Treibitz, 2019) while being able to rapidly process images at up to 60Hz, thus making it suitable for use onboard AUVs as a preprocessing step to enable more robust vision-based behaviours.

翻译：水下基于视觉的复杂行为应用一直滞后于陆地和空中领域的发展，这主要归因于水下图像形成过程中涉及的物理现象导致图像质量下降。光谱选择性光衰减会滤除水下图像中的部分颜色，而后向散射则引入其他颜色，使得水下视觉任务难以执行。当前最先进的水下颜色校正方法通过优化图像形成模型参数来还原水下图像的全光谱色彩。然而，这类方法因主要面向离线颜色校正设计，计算复杂度较高，不利于自主水下机器人（AUV）的实时应用。本文提出了一种新颖的算法DeepSeeColor，该算法将先进的水下图像形成模型与深度学习框架的计算效率相结合。实验表明，DeepSeeColor在性能上与流行的"Sea-Thru"算法（Akkaynak & Treibitz, 2019）相当，同时能够以高达60Hz的频率快速处理图像，因此适用于AUV的机载预处理环节，以支持更鲁棒的基于视觉的行为。