DeepSeeColor：基于深度学习方法的自主水下航行器实时自适应颜色校正
DeepSeeColor: Realtime Adaptive Color Correction for Autonomous Underwater Vehicles via Deep Learning Methods
摘要：
水下复杂视觉行为的成功应用落后于陆地和空中领域的进展。这在很大程度上是由于水下成像中涉及的物理现象导致的图像质量下降。光谱选择性光衰减会从水下图像中排出一些颜色，而反向散射会增加其他颜色，这使得在水下执行基于视觉的任务具有挑战性。最先进的水下色彩校正方法优化了图像形成模型的参数，以恢复水下图像的全光谱色彩。然而，这些方法具有高的计算复杂性，这对于自主水下航行器（AUV）的实时使用是不利的，因为这些方法主要被设计用于离线颜色校正。在这里，我们提出了DeepSeeColor，这是一种新的算法，它将最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。在我们的实验中，我们表明DeepSeeColor提供了与流行的“Sea-Thru”算法（Akkaynak & Treibitz，2019）相当的性能，同时能够以高达60 Hz的频率快速处理图像，从而使其适合用于机载AUV作为预处理步骤，以实现更强大的基于视觉的行为。
英文摘要：
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.
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