Underwater image restoration via adaptive color balance and multi-purpose oriented contrast enhancement

Due to the absorption and the scattering effects of light propagation, underwater images taken by optical devices are often characterized by color casts, low contrast, blurry details, and poor brightness. Current image enhancement methods are difficult to simultaneously deal with multiple degradation problems due to the lack of consideration of the underwater imaging mechanism. To overcome this limitation, this paper proposes an underwater image restoration method via adaptive color balance and multi-purpose oriented contrast enhancement. Concretely, we select a reference channel relying on the highest mean value and simple stretching and use the gray-world assumption to design two gain factors of color compensation, achieving the balanced color distribution of the R, G, and B channels. Then, we fuse multiple priors of backscatter light to build an adaptive standard deviation of a Gaussian filter, separating the pure color veil, which enables us to effectively estimate an accurate backscatter light from different underwater scenes. Finally, we design a multi-purpose-oriented contrast enhancement strategy containing to improve the overall visibility, which takes into account the distribution characteristics of different degradation issues. Extensive experiments on two underwater image datasets show that our method is effective and robust for improving the qualitative and quantitative results of underwater images, i.e., compared with the second-best comparative method, the average <i>e</i> and <i>r</i>¯ values of our method at least increase by 1.03% and 3.78%, which implies that our method can significantly enhance the contrast, the colors, and the details. Meanwhile, our method produces good generalization ability for restoring complex nighttime images.