Our work “DeepCaustics: Classification and Removal of Caustics from Underwater Imagery” will appear as a regular journal publication in IEEE Journal of Oceanic Engineering 2018. This work is co-authored with Timothy Forbes, Mark Goldsmith, Sudhir Mudur, Charalambos Poullis.
Caustics are complex physical phenomena resulting from the projection of light rays being reflected or refracted by a curved surface. In this work, we address the problem of classifying and removing caustics from images and propose a novel solution based on two Convolutional Neural Networks (CNNs): SalienceNet and DeepCaustics. Caustics result in changes in illumination which are continuous in nature, therefore the first network is trained to produce a classification of caustics which is represented as a saliency map of the likelihood of caustics occurring at a pixel. In applications where caustic removal is essential, the second network is trained to generate a caustic-free image. It is extremely hard to generate real ground truth for caustics. We demonstrate how synthetic caustic data can be used for training in such cases, and then transfer the learning to real data. To the best of our knowledge, out of the handful of techniques which have been proposed this is the first time that the complex problem of caustic removal has been reformulated and
addressed as a classification and learning problem. This work is motivated by the real-world challenges in underwater archaeology.