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Underwater image restoration through regularization of coherent structures

Restoration of underwater images plays a vital role in underwater target detection and recognition, underwater robots, underwater rescue, sea organism monitoring, marine geological survey, and real-time navigation. Mostly, physics-based optimization methods do not incorporate structural differences between the guidance and transmission maps (TMs) which affect the performance. In this paper, we propose a method for underwater image restoration by utilizing a robust regularization of coherent structures. The proposed method incorporates the potential structural differences between TM and the guidance map. The optimization of TM is modeled through a nonconvex energy function which consists of data and smoothness terms. The initial TM is taken as a data term whereas the smoothness term contains static and dynamic structural priors. Finally, the optimization problem is solved using majorize-minimize (MM) algorithm. The proposed method is tested on benchmark dataset and its performance is compared with the state-of-the-art methods. The results from the experiments indicate that the proposed regularization scheme adequately improves the TM, which results in high-quality restored images.