Self-Adaptive Colour Calibration of Deep Underwater Images Using FNN and SfM-MVS-Generated Depth Maps

The task of colour restoration on datasets acquired in deep waters with simple equipment such as a camera with strobes is not an easy task. This is due to the lack of a lot of information, such as the water environmental conditions, the geometric setup of the strobes and the camera, and in general, the lack of precisely calibrated setups. It is for these reasons that this study proposes a self-adaptive colour calibration method for underwater (UW) images captured in deep waters with a simple camera and strobe setup. The proposed methodology utilises the scene’s 3D geometry in the form of Structure from Motion and MultiView Stereo (SfM-MVS)-generated depth maps, the well-lit areas of certain images, and a Feedforward Neural Network (FNN) to predict and restore the actual colours of the scene in a UW image dataset.