DeepSeeColor: Realtime Adaptive Color Correction for Autonomous Underwater Vehicles via Deep Learning Methods
This enables more robust vision-based behaviors for autonomous underwater vehicles by providing efficient, real-time preprocessing, though it is incremental as it builds on existing image formation models.
The paper tackles the problem of real-time color correction for underwater images, which is challenging due to degraded image quality from light attenuation and backscattering, and presents DeepSeeColor, a deep learning-based algorithm that achieves comparable performance to the Sea-Thru algorithm while processing images at up to 60Hz.
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.