Change detection in underwater imagery

In this work, we deal with the problem of change detection in an underwater scenario given an unblurred-blurred image pair of a planar scene taken at different times. The blur is primarily due to the dynamic nature of the water surface and its nature is space-invariant in the presence of cyclic water flows. Exploiting the sparsity of the induced blur as well as the occlusions, we propose a distort-difference pipeline that employs an alternating minimization framework to perform change detection in the presence of geometric distortions (skew) as well as photometric degradations (blur and global illumination variations). The method can effectively yield both sharp and blurred occluder maps. Using synthetic as well as real data, we demonstrate how the proposed technique advances the state of the art.