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.

Underwater photography is hard to get right. Special filters, artificial lights, and top-of-the-line underwater cameras can help, but there's still a lot of water between the camera and the object in the photo. We've become accustomed to the blue-green tint of underwater photography. How would the ocean look without water? What are the true colors of a coral reef?

Scientists have created a computer algorithm that can take an underwater photograph and automatically readjust its colors to compensate for the distorting effect of water on light. Researchers Derya Akkaynak and Tali Trebitz started work on the technology – called Sea-thru -- more than three years ago. Akkaynak told Business Insider via email that Sea-thru's mission is to enable huge, artificial intelligence-powered analysis of underwater images. The algorithm effectively adjusts underwater images to make them look like they were taken in broad daylight, making them easier for AI software to analyze. "On underwater images, AI methods generally perform poorly or inconsistently, because water degrades images too severely for automated analysis," she said.

Light behaves differently in water than it does on the surface -- and that behavior creates the blur or green tint common in underwater photographs as well as the haze that blocks out vital details. But thanks to research from an oceanographer and engineer and a new artificial intelligence program called Sea-Thru, that haze and those occluded colors could soon disappear. Besides putting a downer on the photos from that snorkeling trip, the inability to get an accurately colored photo underwater hinders scientific research at a time when concern for coral and ocean health is growing. That's why oceanographer and engineer Derya Akkaynak, along with Tali Treibitz and the University of Haifa, devoted their research to developing an artificial intelligence that can create scientifically accurate colors while removing the haze in underwater photos. As Akkaynak points out in her research, imaging A.I. has exploded in recent years.