DeepSeeColor: Realtime Adaptive Color Correction for Autonomous Underwater Vehicles Via Deep Learning Methods

Successful applications of complex vision-based behaviours underwater have lagged behind progress in terres- trial 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 at- tenuation 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 com- putational 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 [1] 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. OPEN-SOURCE SOFTWARE The datasets collected for and used in this paper are available along with an implementation of DeepSeeColor at: https://warp.whoi.edu/deepseecolor/