An efficient nonlocal variational method with application to underwater image restoration

Light is absorbed and scattered when it travels though water, which causes the captured underwater images often suffering from blurring, low contrast and color degradation. To overcome these problems, we propose a novel variational model based on nonlocal differential operators, in which the underwater image formation model is successfully integrated into the variational framework. The underwater dark channel prior (UDCP) and quad-tree subdivision methods are applied to the construction of underwater image formation model to estimate the transmission map and the global background light. Furthermore, we employ a fast algorithm based on the alternating direction method of multipliers (ADMM) to speed up the solving procedure. To reproduce color saturation, we perform a Gamma correction operation on the recovered image. Both the real underwater image application test and the simulation experiment demonstrate that the proposed underwater nonlocal total variational (UNLTV) approach achieves superb performance on dehazing, denoising, and improving the visibility of underwater images. In addition, six state-of-the-art algorithms are compared under different challenging scenes to evaluate their effectiveness and robustness. Extensive qualitative and quantitative experimental comparisons further validate the superiority of the proposed method.